{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stock Price Prediction using the Quantopian Trading Platform"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The notebook linear_regression.ipynb contains examples for the prediction of stock prices using OLS with statsmodels and sklearn, as well as ridge and lasso models. \n",
    "\n",
    "It is designed to run as a notebook on the Quantopian research platform and relies on the factor_library introduced in Chapter 4, Research and Evaluation of Alpha Factors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to run this notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook is written for the Quantopian [research environment](https://www.quantopian.com/research). You can upload it after signing up and execute it on the Quantopian platform to gain access to the datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from time import time\n",
    "import talib\n",
    "import re\n",
    "from statsmodels.api import OLS\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from scipy.stats import spearmanr\n",
    "from sklearn.linear_model import LinearRegression, Ridge, RidgeCV, Lasso, LassoCV, LogisticRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from quantopian.research import run_pipeline\n",
    "from quantopian.pipeline import Pipeline, factors, filters, classifiers\n",
    "from quantopian.pipeline.data.builtin import USEquityPricing\n",
    "from quantopian.pipeline.factors import (Latest, \n",
    "                                         Returns, \n",
    "                                         AverageDollarVolume, \n",
    "                                         SimpleMovingAverage,\n",
    "                                         EWMA,\n",
    "                                         BollingerBands,\n",
    "                                         CustomFactor,\n",
    "                                         MarketCap,\n",
    "                                        SimpleBeta)\n",
    "from quantopian.pipeline.filters import QTradableStocksUS, StaticAssets\n",
    "from quantopian.pipeline.data.quandl import fred_usdontd156n as libor\n",
    "from empyrical import max_drawdown, sortino_ratio\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Sources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "################\n",
    "# Fundamentals #\n",
    "################\n",
    "\n",
    "# Morningstar fundamentals (2002 - Ongoing)\n",
    "# https://www.quantopian.com/help/fundamentals\n",
    "from quantopian.pipeline.data import Fundamentals\n",
    "\n",
    "#####################\n",
    "# Analyst Estimates #\n",
    "#####################\n",
    "\n",
    "# Earnings Surprises - Zacks (27 May 2006 - Ongoing)\n",
    "# https://www.quantopian.com/data/zacks/earnings_surprises\n",
    "from quantopian.pipeline.data.zacks import EarningsSurprises\n",
    "from quantopian.pipeline.factors.zacks import BusinessDaysSinceEarningsSurprisesAnnouncement\n",
    "\n",
    "##########\n",
    "# Events #\n",
    "##########\n",
    "\n",
    "# Buyback Announcements - EventVestor (01 Jun 2007 - Ongoing)\n",
    "# https://www.quantopian.com/data/eventvestor/buyback_auth\n",
    "from quantopian.pipeline.data.eventvestor import BuybackAuthorizations\n",
    "from quantopian.pipeline.factors.eventvestor import BusinessDaysSinceBuybackAuth\n",
    "\n",
    "# CEO Changes - EventVestor (01 Jan 2007 - Ongoing)\n",
    "# https://www.quantopian.com/data/eventvestor/ceo_change\n",
    "from quantopian.pipeline.data.eventvestor import CEOChangeAnnouncements\n",
    "\n",
    "# Dividends - EventVestor (01 Jan 2007 - Ongoing)\n",
    "# https://www.quantopian.com/data/eventvestor/dividends\n",
    "from quantopian.pipeline.data.eventvestor import (\n",
    "    DividendsByExDate,\n",
    "    DividendsByPayDate,\n",
    "    DividendsByAnnouncementDate,\n",
    ")\n",
    "from quantopian.pipeline.factors.eventvestor import (\n",
    "    BusinessDaysSincePreviousExDate,\n",
    "    BusinessDaysUntilNextExDate,\n",
    "    BusinessDaysSinceDividendAnnouncement,\n",
    ")\n",
    "\n",
    "# Earnings Calendar - EventVestor (01 Jan 2007 - Ongoing)\n",
    "# https://www.quantopian.com/data/eventvestor/earnings_calendar\n",
    "from quantopian.pipeline.data.eventvestor import EarningsCalendar\n",
    "from quantopian.pipeline.factors.eventvestor import (\n",
    "    BusinessDaysUntilNextEarnings,\n",
    "    BusinessDaysSincePreviousEarnings\n",
    ")\n",
    "\n",
    "# 13D Filings - EventVestor (01 Jan 2007 - Ongoing)\n",
    "# https://www.quantopian.com/data/eventvestor/_13d_filings\n",
    "from quantopian.pipeline.data.eventvestor import _13DFilings\n",
    "from quantopian.pipeline.factors.eventvestor import BusinessDaysSince13DFilingsDate\n",
    "\n",
    "#############\n",
    "# Sentiment #\n",
    "#############\n",
    "\n",
    "# News Sentiment - Sentdex Sentiment Analysis (15 Oct 2012 - Ongoing)\n",
    "# https://www.quantopian.com/data/sentdex/sentiment\n",
    "from quantopian.pipeline.data.sentdex import sentiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to select a universe of equities and a time horizon, build and transform alpha factors that we will use as features, calculate forward returns that we aim to predict, and potentially clean our data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time horizon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# trading days per period\n",
    "MONTH = 21\n",
    "YEAR = 12 * MONTH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "START = '2014-01-01'\n",
    "END = '2015-12-31'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Universe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use equity data for the years 2014 and 2015 from a custom Q100US universe that uses built-in filters, factors, and classifiers to select the 100 stocks with the highest average dollar volume of the last 200 trading days filtered by additional default criteria (see Quantopian docs linked on GitHub for detail). The universe dynamically updates based on the filter criteria so that, while there are 100 stocks at any given point, there may be more than 100 distinct equities in the sample:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Q100US():\n",
    "    return filters.make_us_equity_universe(\n",
    "        target_size=100,\n",
    "        rankby=factors.AverageDollarVolume(window_length=200),\n",
    "        mask=filters.default_us_equity_universe_mask(),\n",
    "        groupby=classifiers.fundamentals.Sector(),\n",
    "        max_group_weight=0.3,\n",
    "        smoothing_func=lambda f: f.downsample('month_start'),\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# UNIVERSE = StaticAssets(symbols(['MSFT', 'AAPL']))\n",
    "UNIVERSE = Q100US()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Factor Transformations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class AnnualizedData(CustomFactor):\n",
    "    # Get the sum of the last 4 reported values\n",
    "    window_length = 260\n",
    "\n",
    "    def compute(self, today, assets, out, asof_date, values):\n",
    "        for asset in range(len(assets)):\n",
    "            # unique asof dates indicate availability of new figures\n",
    "            _, filing_dates = np.unique(asof_date[:, asset], return_index=True)\n",
    "            quarterly_values = values[filing_dates[-4:], asset]\n",
    "            # ignore annual windows with <4 quarterly data points\n",
    "            if len(~np.isnan(quarterly_values)) != 4:\n",
    "                out[asset] = np.nan\n",
    "            else:\n",
    "                out[asset] = np.sum(quarterly_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class AnnualAvg(CustomFactor):\n",
    "    window_length = 252\n",
    "    \n",
    "    def compute(self, today, assets, out, values):\n",
    "        out[:] = (values[0] + values[-1])/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def factor_pipeline(factors):\n",
    "    start = time()\n",
    "    pipe = Pipeline({k: v(mask=UNIVERSE).rank() for k, v in factors.items()},\n",
    "                    screen=UNIVERSE)\n",
    "    result = run_pipeline(pipe, start_date=START, end_date=END)\n",
    "    return result, time() - start"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Factor Library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Value Factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ValueFactors:\n",
    "    \"\"\"Definitions of factors for cross-sectional trading algorithms\"\"\"\n",
    "    \n",
    "    @staticmethod\n",
    "    def PriceToSalesTTM(**kwargs):\n",
    "        \"\"\"Last closing price divided by sales per share\"\"\"        \n",
    "        return Fundamentals.ps_ratio.latest\n",
    "\n",
    "    @staticmethod\n",
    "    def PriceToEarningsTTM(**kwargs):\n",
    "        \"\"\"Closing price divided by earnings per share (EPS)\"\"\"\n",
    "        return Fundamentals.pe_ratio.latest\n",
    " \n",
    "    @staticmethod\n",
    "    def PriceToDilutedEarningsTTM(mask):\n",
    "        \"\"\"Closing price divided by diluted EPS\"\"\"\n",
    "        last_close = USEquityPricing.close.latest\n",
    "        diluted_eps = AnnualizedData(inputs = [Fundamentals.diluted_eps_earnings_reports_asof_date,\n",
    "                                               Fundamentals.diluted_eps_earnings_reports],\n",
    "                                     mask=mask)\n",
    "        return last_close / diluted_eps\n",
    "\n",
    "    @staticmethod\n",
    "    def PriceToForwardEarnings(**kwargs):\n",
    "        \"\"\"Price to Forward Earnings\"\"\"\n",
    "        return Fundamentals.forward_pe_ratio.latest\n",
    "    \n",
    "    @staticmethod\n",
    "    def DividendYield(**kwargs):\n",
    "        \"\"\"Dividends per share divided by closing price\"\"\"\n",
    "        return Fundamentals.trailing_dividend_yield.latest\n",
    "\n",
    "    @staticmethod\n",
    "    def PriceToFCF(mask):\n",
    "        \"\"\"Price to Free Cash Flow\"\"\"\n",
    "        last_close = USEquityPricing.close.latest\n",
    "        fcf_share = AnnualizedData(inputs = [Fundamentals.fcf_per_share_asof_date,\n",
    "                                             Fundamentals.fcf_per_share],\n",
    "                                   mask=mask)\n",
    "        return last_close / fcf_share\n",
    "\n",
    "    @staticmethod\n",
    "    def PriceToOperatingCashflow(mask):\n",
    "        \"\"\"Last Close divided by Operating Cash Flows\"\"\"\n",
    "        last_close = USEquityPricing.close.latest\n",
    "        cfo_per_share = AnnualizedData(inputs = [Fundamentals.cfo_per_share_asof_date,\n",
    "                                                 Fundamentals.cfo_per_share],\n",
    "                                       mask=mask)        \n",
    "        return last_close / cfo_per_share\n",
    "\n",
    "    @staticmethod\n",
    "    def PriceToBook(mask):\n",
    "        \"\"\"Closing price divided by book value\"\"\"\n",
    "        last_close = USEquityPricing.close.latest\n",
    "        book_value_per_share = AnnualizedData(inputs = [Fundamentals.book_value_per_share_asof_date,\n",
    "                                              Fundamentals.book_value_per_share],\n",
    "                                             mask=mask)        \n",
    "        return last_close / book_value_per_share\n",
    "\n",
    "\n",
    "    @staticmethod\n",
    "    def EVToFCF(mask):\n",
    "        \"\"\"Enterprise Value divided by Free Cash Flows\"\"\"\n",
    "        fcf = AnnualizedData(inputs = [Fundamentals.free_cash_flow_asof_date,\n",
    "                                       Fundamentals.free_cash_flow],\n",
    "                             mask=mask)\n",
    "        return Fundamentals.enterprise_value.latest / fcf\n",
    "\n",
    "    @staticmethod\n",
    "    def EVToEBITDA(mask):\n",
    "        \"\"\"Enterprise Value to Earnings Before Interest, Taxes, Deprecation and Amortization (EBITDA)\"\"\"\n",
    "        ebitda = AnnualizedData(inputs = [Fundamentals.ebitda_asof_date,\n",
    "                                          Fundamentals.ebitda],\n",
    "                                mask=mask)\n",
    "\n",
    "        return Fundamentals.enterprise_value.latest / ebitda\n",
    "\n",
    "    @staticmethod\n",
    "    def EBITDAYield(mask):\n",
    "        \"\"\"EBITDA divided by latest close\"\"\"\n",
    "        ebitda = AnnualizedData(inputs = [Fundamentals.ebitda_asof_date,\n",
    "                                          Fundamentals.ebitda],\n",
    "                                mask=mask)\n",
    "        return USEquityPricing.close.latest / ebitda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "VALUE_FACTORS = {\n",
    "    'DividendYield'            : ValueFactors.DividendYield,\n",
    "    'EBITDAYield'              : ValueFactors.EBITDAYield,\n",
    "    'EVToEBITDA'               : ValueFactors.EVToEBITDA,\n",
    "    'EVToFCF'                  : ValueFactors.EVToFCF,\n",
    "    'PriceToBook'              : ValueFactors.PriceToBook,\n",
    "    'PriceToDilutedEarningsTTM': ValueFactors.PriceToDilutedEarningsTTM,\n",
    "    'PriceToEarningsTTM'       : ValueFactors.PriceToEarningsTTM,\n",
    "    'PriceToFCF'               : ValueFactors.PriceToFCF,\n",
    "    'PriceToForwardEarnings'   : ValueFactors.PriceToForwardEarnings,\n",
    "    'PriceToOperatingCashflow' : ValueFactors.PriceToOperatingCashflow,\n",
    "    'PriceToSalesTTM'          : ValueFactors.PriceToSalesTTM,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/numpy/lib/arraysetops.py:200: FutureWarning: In the future, NAT != NAT will be True rather than False.\n",
      "  flag = np.concatenate(([True], aux[1:] != aux[:-1]))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline run time 91.43 secs\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 50362 entries, (2014-01-02 00:00:00+00:00, Equity(24 [AAPL])) to (2015-12-31 00:00:00+00:00, Equity(47208 [GPRO]))\n",
      "Data columns (total 11 columns):\n",
      "DividendYield                40772 non-null float64\n",
      "EBITDAYield                  49823 non-null float64\n",
      "EVToEBITDA                   49823 non-null float64\n",
      "EVToFCF                      46400 non-null float64\n",
      "PriceToBook                  50343 non-null float64\n",
      "PriceToDilutedEarningsTTM    50215 non-null float64\n",
      "PriceToEarningsTTM           48956 non-null float64\n",
      "PriceToFCF                   49133 non-null float64\n",
      "PriceToForwardEarnings       39607 non-null float64\n",
      "PriceToOperatingCashflow     50343 non-null float64\n",
      "PriceToSalesTTM              50362 non-null float64\n",
      "dtypes: float64(11)\n",
      "memory usage: 4.6+ MB\n"
     ]
    }
   ],
   "source": [
    "value_factors, t = factor_pipeline(VALUE_FACTORS)\n",
    "print('Pipeline run time {:.2f} secs'.format(t))\n",
    "value_factors.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Momentum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MomentumFactors:\n",
    "    \"\"\"Custom Momentum Factors\"\"\"\n",
    "    class PercentAboveLow(CustomFactor):\n",
    "        \"\"\"Percentage of current close above low \n",
    "        in lookback window of window_length days\n",
    "        \"\"\"\n",
    "        inputs = [USEquityPricing.close]\n",
    "        window_length = 252\n",
    "\n",
    "        def compute(self, today, assets, out, close):\n",
    "            out[:] = close[-1] / np.min(close, axis=0) - 1\n",
    "\n",
    "    class PercentBelowHigh(CustomFactor):\n",
    "        \"\"\"Percentage of current close below high \n",
    "        in lookback window of window_length days\n",
    "        \"\"\"\n",
    "        \n",
    "        inputs = [USEquityPricing.close]\n",
    "        window_length = 252\n",
    "            \n",
    "        def compute(self, today, assets, out, close):\n",
    "            out[:] = close[-1] / np.max(close, axis=0) - 1\n",
    "\n",
    "    @staticmethod\n",
    "    def make_dx(timeperiod=14):\n",
    "        class DX(CustomFactor):\n",
    "            \"\"\"Directional Movement Index\"\"\"\n",
    "            inputs = [USEquityPricing.high, \n",
    "                      USEquityPricing.low, \n",
    "                      USEquityPricing.close]\n",
    "            window_length = timeperiod + 1\n",
    "            \n",
    "            def compute(self, today, assets, out, high, low, close):\n",
    "                out[:] = [talib.DX(high[:, i], \n",
    "                                   low[:, i], \n",
    "                                   close[:, i], \n",
    "                                   timeperiod=timeperiod)[-1] \n",
    "                          for i in range(len(assets))]\n",
    "        return DX  \n",
    "\n",
    "    @staticmethod\n",
    "    def make_mfi(timeperiod=14):\n",
    "        class MFI(CustomFactor):\n",
    "            \"\"\"Money Flow Index\"\"\"\n",
    "            inputs = [USEquityPricing.high, \n",
    "                      USEquityPricing.low, \n",
    "                      USEquityPricing.close,\n",
    "                      USEquityPricing.volume]\n",
    "            window_length = timeperiod + 1\n",
    "            \n",
    "            def compute(self, today, assets, out, high, low, close, vol):\n",
    "                out[:] = [talib.MFI(high[:, i], \n",
    "                                    low[:, i], \n",
    "                                    close[:, i],\n",
    "                                    vol[:, i],\n",
    "                                    timeperiod=timeperiod)[-1] \n",
    "                          for i in range(len(assets))]\n",
    "        return MFI           \n",
    "\n",
    "    @staticmethod\n",
    "    def make_oscillator(fastperiod=12, slowperiod=26, matype=0):\n",
    "        class PPO(CustomFactor):\n",
    "            \"\"\"12/26-Day Percent Price Oscillator\"\"\"\n",
    "            inputs = [USEquityPricing.close]\n",
    "            window_length = slowperiod\n",
    "\n",
    "            def compute(self, today, assets, out, close_prices):\n",
    "                out[:] = [talib.PPO(close,\n",
    "                                    fastperiod=fastperiod,\n",
    "                                    slowperiod=slowperiod, \n",
    "                                    matype=matype)[-1]\n",
    "                         for close in close_prices.T]\n",
    "        return PPO\n",
    "\n",
    "    @staticmethod\n",
    "    def make_stochastic_oscillator(fastk_period=5, slowk_period=3, slowd_period=3, \n",
    "                                   slowk_matype=0, slowd_matype=0):                \n",
    "        class StochasticOscillator(CustomFactor):\n",
    "            \"\"\"20-day Stochastic Oscillator \"\"\"\n",
    "            inputs = [USEquityPricing.high, \n",
    "                      USEquityPricing.low, \n",
    "                      USEquityPricing.close]\n",
    "            outputs = ['slowk', 'slowd']\n",
    "            window_length = fastk_period * 2\n",
    "            \n",
    "            def compute(self, today, assets, out, high, low, close):\n",
    "                slowk, slowd = [talib.STOCH(high[:, i],\n",
    "                                            low[:, i],\n",
    "                                            close[:, i], \n",
    "                                            fastk_period=fastk_period,\n",
    "                                            slowk_period=slowk_period, \n",
    "                                            slowk_matype=slowk_matype, \n",
    "                                            slowd_period=slowd_period, \n",
    "                                            slowd_matype=slowd_matype)[-1] \n",
    "                                for i in range(len(assets))]\n",
    "\n",
    "                out.slowk[:], out.slowd[:] = slowk[-1], slowd[-1]\n",
    "        return StochasticOscillator\n",
    "    \n",
    "    @staticmethod\n",
    "    def make_trendline(timeperiod=252):                \n",
    "        class Trendline(CustomFactor):\n",
    "            inputs = [USEquityPricing.close]\n",
    "            \"\"\"52-Week Trendline\"\"\"\n",
    "            window_length = timeperiod\n",
    "\n",
    "            def compute(self, today, assets, out, close_prices):\n",
    "                out[:] = [talib.LINEARREG_SLOPE(close, \n",
    "                                   timeperiod=timeperiod)[-1] \n",
    "                          for close in close_prices.T]\n",
    "        return Trendline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "MOMENTUM_FACTORS = {\n",
    "    'Percent Above Low'            : MomentumFactors.PercentAboveLow,\n",
    "    'Percent Below High'           : MomentumFactors.PercentBelowHigh,\n",
    "    'Price Oscillator'             : MomentumFactors.make_oscillator(),\n",
    "    'Money Flow Index'             : MomentumFactors.make_mfi(),\n",
    "    'Directional Movement Index'   : MomentumFactors.make_dx(),\n",
    "    'Trendline'                    : MomentumFactors.make_trendline()\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline run time 20.68 secs\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 50362 entries, (2014-01-02 00:00:00+00:00, Equity(24 [AAPL])) to (2015-12-31 00:00:00+00:00, Equity(47208 [GPRO]))\n",
      "Data columns (total 6 columns):\n",
      "Directional Movement Index    50362 non-null float64\n",
      "Money Flow Index              50362 non-null float64\n",
      "Percent Above Low             49536 non-null float64\n",
      "Percent Below High            49536 non-null float64\n",
      "Price Oscillator              50355 non-null float64\n",
      "Trendline                     49536 non-null float64\n",
      "dtypes: float64(6)\n",
      "memory usage: 2.7+ MB\n"
     ]
    }
   ],
   "source": [
    "momentum_factors, t = factor_pipeline(MOMENTUM_FACTORS)\n",
    "print('Pipeline run time {:.2f} secs'.format(t))\n",
    "momentum_factors.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Efficiency Factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "class EfficiencyFactors:\n",
    "\n",
    "    @staticmethod\n",
    "    def CapexToAssets(mask):\n",
    "        \"\"\"Capital Expenditure divided by Total Assets\"\"\"\n",
    "        capex = AnnualizedData(inputs = [Fundamentals.capital_expenditure_asof_date,\n",
    "                                         Fundamentals.capital_expenditure],\n",
    "                                     mask=mask)   \n",
    "        assets = Fundamentals.total_assets.latest\n",
    "        return - capex / assets\n",
    "\n",
    "    @staticmethod\n",
    "    def CapexToSales(mask):\n",
    "        \"\"\"Capital Expenditure divided by Total Revenue\"\"\"\n",
    "        capex = AnnualizedData(inputs = [Fundamentals.capital_expenditure_asof_date,\n",
    "                                         Fundamentals.capital_expenditure],\n",
    "                                     mask=mask)   \n",
    "        revenue = AnnualizedData(inputs = [Fundamentals.total_revenue_asof_date,\n",
    "                                         Fundamentals.total_revenue],\n",
    "                                     mask=mask)         \n",
    "        return - capex / revenue\n",
    "  \n",
    "    @staticmethod\n",
    "    def CapexToFCF(mask):\n",
    "        \"\"\"Capital Expenditure divided by Free Cash Flows\"\"\"\n",
    "        capex = AnnualizedData(inputs = [Fundamentals.capital_expenditure_asof_date,\n",
    "                                         Fundamentals.capital_expenditure],\n",
    "                                     mask=mask)   \n",
    "        free_cash_flow = AnnualizedData(inputs = [Fundamentals.free_cash_flow_asof_date,\n",
    "                                         Fundamentals.free_cash_flow],\n",
    "                                     mask=mask)         \n",
    "        return - capex / free_cash_flow\n",
    "\n",
    "    @staticmethod\n",
    "    def EBITToAssets(mask):\n",
    "        \"\"\"Earnings Before Interest and Taxes (EBIT) divided by Total Assets\"\"\"\n",
    "        ebit = AnnualizedData(inputs = [Fundamentals.ebit_asof_date,\n",
    "                                         Fundamentals.ebit],\n",
    "                                     mask=mask)   \n",
    "        assets = Fundamentals.total_assets.latest\n",
    "        return ebit / assets\n",
    "    \n",
    "    @staticmethod\n",
    "    def CFOToAssets(mask):\n",
    "        \"\"\"Operating Cash Flows divided by Total Assets\"\"\"\n",
    "        cfo = AnnualizedData(inputs = [Fundamentals.operating_cash_flow_asof_date,\n",
    "                                         Fundamentals.operating_cash_flow],\n",
    "                                     mask=mask)   \n",
    "        assets = Fundamentals.total_assets.latest\n",
    "        return cfo / assets \n",
    "    \n",
    "    @staticmethod\n",
    "    def RetainedEarningsToAssets(mask):\n",
    "        \"\"\"Retained Earnings divided by Total Assets\"\"\"\n",
    "        retained_earnings = AnnualizedData(inputs = [Fundamentals.retained_earnings_asof_date,\n",
    "                                         Fundamentals.retained_earnings],\n",
    "                                     mask=mask)   \n",
    "        assets = Fundamentals.total_assets.latest\n",
    "        return retained_earnings / assets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "EFFICIENCY_FACTORS = {\n",
    "    'CFO To Assets' :EfficiencyFactors.CFOToAssets,\n",
    "    'Capex To Assets' :EfficiencyFactors.CapexToAssets,\n",
    "    'Capex To FCF' :EfficiencyFactors.CapexToFCF,\n",
    "    'Capex To Sales' :EfficiencyFactors.CapexToSales,\n",
    "    'EBIT To Assets' :EfficiencyFactors.EBITToAssets,\n",
    "    'Retained Earnings To Assets' :EfficiencyFactors.RetainedEarningsToAssets\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline run time 38.54 secs\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 50362 entries, (2014-01-02 00:00:00+00:00, Equity(24 [AAPL])) to (2015-12-31 00:00:00+00:00, Equity(47208 [GPRO]))\n",
      "Data columns (total 6 columns):\n",
      "CFO To Assets                  50351 non-null float64\n",
      "Capex To Assets                46997 non-null float64\n",
      "Capex To FCF                   45799 non-null float64\n",
      "Capex To Sales                 46997 non-null float64\n",
      "EBIT To Assets                 46635 non-null float64\n",
      "Retained Earnings To Assets    50349 non-null float64\n",
      "dtypes: float64(6)\n",
      "memory usage: 2.7+ MB\n"
     ]
    }
   ],
   "source": [
    "efficiency_factors, t = factor_pipeline(EFFICIENCY_FACTORS)\n",
    "print('Pipeline run time {:.2f} secs'.format(t))\n",
    "efficiency_factors.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Risk Factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RiskFactors:\n",
    "\n",
    "    @staticmethod\n",
    "    def LogMarketCap(mask):\n",
    "        \"\"\"Log of Market Capitalization log(Close Price * Shares Outstanding)\"\"\"\n",
    "        return np.log(MarketCap(mask=mask))\n",
    " \n",
    "    class DownsideRisk(CustomFactor):\n",
    "        \"\"\"Mean returns divided by std of 1yr daily losses (Sortino Ratio)\"\"\"\n",
    "        inputs = [USEquityPricing.close]\n",
    "        window_length = 252\n",
    "\n",
    "        def compute(self, today, assets, out, close):\n",
    "            ret = pd.DataFrame(close).pct_change()\n",
    "            out[:] = ret.mean().div(ret.where(ret<0).std())\n",
    "\n",
    "    @staticmethod\n",
    "    def MarketBeta(**kwargs):\n",
    "        \"\"\"Slope of 1-yr regression of price returns against index returns\"\"\"\n",
    "        return SimpleBeta(target=symbols('SPY'), regression_length=252) \n",
    "\n",
    "    class DownsideBeta(CustomFactor):\n",
    "        \"\"\"Slope of 1yr regression of returns on negative index returns\"\"\"\n",
    "        inputs = [USEquityPricing.close]\n",
    "        window_length = 252\n",
    "\n",
    "        def compute(self, today, assets, out, close):\n",
    "            t = len(close)\n",
    "            assets = pd.DataFrame(close).pct_change()\n",
    "            \n",
    "            start_date = (today - pd.DateOffset(years=1)).strftime('%Y-%m-%d')\n",
    "            spy = get_pricing('SPY', \n",
    "                              start_date=start_date, \n",
    "                              end_date=today.strftime('%Y-%m-%d')).reset_index(drop=True)\n",
    "            spy_neg_ret = (spy\n",
    "                           .close_price\n",
    "                           .iloc[-t:]\n",
    "                           .pct_change()\n",
    "                           .pipe(lambda x: x.where(x<0)))\n",
    "    \n",
    "            out[:] = assets.apply(lambda x: x.cov(spy_neg_ret)).div(spy_neg_ret.var())         \n",
    "\n",
    "    class Vol3M(CustomFactor):\n",
    "        \"\"\"3-month Volatility: Standard deviation of returns over 3 months\"\"\"\n",
    "\n",
    "        inputs = [USEquityPricing.close]\n",
    "        window_length = 63\n",
    "\n",
    "        def compute(self, today, assets, out, close):\n",
    "            out[:] = np.log1p(pd.DataFrame(close).pct_change()).std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "RISK_FACTORS = {\n",
    "    'Log Market Cap' : RiskFactors.LogMarketCap,\n",
    "    'Downside Risk'  : RiskFactors.DownsideRisk,\n",
    "    'Index Beta'     : RiskFactors.MarketBeta,\n",
    "#     'Downside Beta'  : RiskFactors.DownsideBeta,    \n",
    "    'Volatility 3M'  : RiskFactors.Vol3M,    \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline run time 46.76 secs\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 50362 entries, (2014-01-02 00:00:00+00:00, Equity(24 [AAPL])) to (2015-12-31 00:00:00+00:00, Equity(47208 [GPRO]))\n",
      "Data columns (total 4 columns):\n",
      "Downside Risk     50362 non-null float64\n",
      "Index Beta        50079 non-null float64\n",
      "Log Market Cap    50362 non-null float64\n",
      "Volatility 3M     50362 non-null float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 1.9+ MB\n"
     ]
    }
   ],
   "source": [
    "risk_factors, t = factor_pipeline(RISK_FACTORS)\n",
    "print('Pipeline run time {:.2f} secs'.format(t))\n",
    "risk_factors.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Growth Factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def growth_pipeline():\n",
    "    revenue = AnnualizedData(inputs = [Fundamentals.total_revenue_asof_date,\n",
    "                                       Fundamentals.total_revenue],\n",
    "                             mask=UNIVERSE)\n",
    "    eps = AnnualizedData(inputs = [Fundamentals.diluted_eps_earnings_reports_asof_date,\n",
    "                                       Fundamentals.diluted_eps_earnings_reports],\n",
    "                             mask=UNIVERSE)    \n",
    "\n",
    "    return Pipeline({'Sales': revenue,\n",
    "                     'EPS': eps,\n",
    "                     'Total Assets': Fundamentals.total_assets.latest,\n",
    "                     'Net Debt': Fundamentals.net_debt.latest},\n",
    "                    screen=UNIVERSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline run time 23.05 secs\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 50362 entries, (2014-01-02 00:00:00+00:00, Equity(24 [AAPL])) to (2015-12-31 00:00:00+00:00, Equity(47208 [GPRO]))\n",
      "Data columns (total 12 columns):\n",
      "EPS                        50215 non-null float64\n",
      "Net Debt                   47413 non-null float64\n",
      "Sales                      50351 non-null float64\n",
      "Total Assets               50362 non-null float64\n",
      "EPS Growth 3M              50152 non-null float64\n",
      "EPS Growth 12M             49963 non-null float64\n",
      "Net Debt Growth 3M         47350 non-null float64\n",
      "Net Debt Growth 12M        47171 non-null float64\n",
      "Sales Growth 3M            50288 non-null float64\n",
      "Sales Growth 12M           50099 non-null float64\n",
      "Total Assets Growth 3M     50299 non-null float64\n",
      "Total Assets Growth 12M    50110 non-null float64\n",
      "dtypes: float64(12)\n",
      "memory usage: 5.0+ MB\n"
     ]
    }
   ],
   "source": [
    "start_timer = time()\n",
    "growth_factors = run_pipeline(growth_pipeline(), start_date=START, end_date=END)\n",
    "\n",
    "for col in growth_result.columns:\n",
    "    for month in [3, 12]:\n",
    "        new_col = col + ' Growth {}M'.format(month)\n",
    "        kwargs = {new_col: growth_factors[col].pct_change(month*MONTH).groupby(level=1).rank()}        \n",
    "        growth_factors = growth_factors.assign(**kwargs)\n",
    "print('Pipeline run time {:.2f} secs'.format(time() - start_timer))\n",
    "growth_factors.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quality Factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "class QualityFactors:\n",
    "    \n",
    "    @staticmethod\n",
    "    def AssetTurnover(mask):\n",
    "        \"\"\"Sales divided by average of year beginning and year end assets\"\"\"\n",
    "\n",
    "        assets = AnnualAvg(inputs=[Fundamentals.total_assets],\n",
    "                           mask=mask)\n",
    "        sales = AnnualizedData([Fundamentals.total_revenue_asof_date,\n",
    "                                Fundamentals.total_revenue], mask=mask)\n",
    "        return sales / assets\n",
    "  \n",
    "    @staticmethod\n",
    "    def CurrentRatio(mask):\n",
    "        \"\"\"Total current assets divided by total current liabilities\"\"\"\n",
    "\n",
    "        assets = Fundamentals.current_assets.latest\n",
    "        liabilities = Fundamentals.current_liabilities.latest\n",
    "        return assets / liabilities\n",
    "    \n",
    "    @staticmethod\n",
    "    def AssetToEquityRatio(mask):\n",
    "        \"\"\"Total current assets divided by common equity\"\"\"\n",
    "\n",
    "        assets = Fundamentals.current_assets.latest\n",
    "        equity = Fundamentals.common_stock.latest\n",
    "        return assets / equity    \n",
    "\n",
    "    \n",
    "    @staticmethod\n",
    "    def InterestCoverage(mask):\n",
    "        \"\"\"EBIT divided by interest expense\"\"\"\n",
    "\n",
    "        ebit = AnnualizedData(inputs = [Fundamentals.ebit_asof_date,\n",
    "                                        Fundamentals.ebit], mask=mask)  \n",
    "        \n",
    "        interest_expense = AnnualizedData(inputs = [Fundamentals.interest_expense_asof_date,\n",
    "                                        Fundamentals.interest_expense], mask=mask)\n",
    "        return ebit / interest_expense\n",
    "\n",
    "    @staticmethod\n",
    "    def DebtToAssetRatio(mask):\n",
    "        \"\"\"Total Debts divided by Total Assets\"\"\"\n",
    "\n",
    "        debt = Fundamentals.total_debt.latest\n",
    "        assets = Fundamentals.total_assets.latest\n",
    "        return debt / assets\n",
    "    \n",
    "    @staticmethod\n",
    "    def DebtToEquityRatio(mask):\n",
    "        \"\"\"Total Debts divided by Common Stock Equity\"\"\"\n",
    "\n",
    "        debt = Fundamentals.total_debt.latest\n",
    "        equity = Fundamentals.common_stock.latest\n",
    "        return debt / equity    \n",
    "\n",
    "    @staticmethod\n",
    "    def WorkingCapitalToAssets(mask):\n",
    "        \"\"\"Current Assets less Current liabilities (Working Capital) divided by Assets\"\"\"\n",
    "\n",
    "        working_capital = Fundamentals.working_capital.latest\n",
    "        assets = Fundamentals.total_assets.latest\n",
    "        return working_capital / assets\n",
    " \n",
    "    @staticmethod\n",
    "    def WorkingCapitalToSales(mask):\n",
    "        \"\"\"Current Assets less Current liabilities (Working Capital), divided by Sales\"\"\"\n",
    "\n",
    "        working_capital = Fundamentals.working_capital.latest\n",
    "        sales = AnnualizedData([Fundamentals.total_revenue_asof_date,\n",
    "                                Fundamentals.total_revenue], mask=mask)        \n",
    "        return working_capital / sales          \n",
    "       \n",
    "        \n",
    "    class MertonsDD(CustomFactor):\n",
    "        \"\"\"Merton's Distance to Default \"\"\"\n",
    "        \n",
    "        inputs = [Fundamentals.total_assets,\n",
    "                  Fundamentals.total_liabilities, \n",
    "                  libor.value, \n",
    "                  USEquityPricing.close]\n",
    "        window_length = 252\n",
    "\n",
    "        def compute(self, today, assets, out, tot_assets, tot_liabilities, r, close):\n",
    "            mertons = []\n",
    "\n",
    "            for col_assets, col_liabilities, col_r, col_close in zip(tot_assets.T, tot_liabilities.T,\n",
    "                                                                     r.T, close.T):\n",
    "                vol_1y = np.nanstd(col_close)\n",
    "                numerator = np.log(\n",
    "                        col_assets[-1] / col_liabilities[-1]) + ((252 * col_r[-1]) - ((vol_1y ** 2) / 2))\n",
    "                mertons.append(numerator / vol_1y)\n",
    "\n",
    "            out[:] = mertons            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "QUALITY_FACTORS = {\n",
    "    'AssetToEquityRatio'    : QualityFactors.AssetToEquityRatio,\n",
    "    'AssetTurnover'         : QualityFactors.AssetTurnover,\n",
    "    'CurrentRatio'          : QualityFactors.CurrentRatio,\n",
    "    'DebtToAssetRatio'      : QualityFactors.DebtToAssetRatio,\n",
    "    'DebtToEquityRatio'     : QualityFactors.DebtToEquityRatio,\n",
    "    'InterestCoverage'      : QualityFactors.InterestCoverage,\n",
    "    'MertonsDD'             : QualityFactors.MertonsDD,\n",
    "    'WorkingCapitalToAssets': QualityFactors.WorkingCapitalToAssets,\n",
    "    'WorkingCapitalToSales' : QualityFactors.WorkingCapitalToSales,\n",
    "}\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline run time 37.24 secs\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 50362 entries, (2014-01-02 00:00:00+00:00, Equity(24 [AAPL])) to (2015-12-31 00:00:00+00:00, Equity(47208 [GPRO]))\n",
      "Data columns (total 9 columns):\n",
      "AssetToEquityRatio        45176 non-null float64\n",
      "AssetTurnover             50314 non-null float64\n",
      "CurrentRatio              45680 non-null float64\n",
      "DebtToAssetRatio          50080 non-null float64\n",
      "DebtToEquityRatio         48492 non-null float64\n",
      "InterestCoverage          35250 non-null float64\n",
      "MertonsDD                 50362 non-null float64\n",
      "WorkingCapitalToAssets    45680 non-null float64\n",
      "WorkingCapitalToSales     45669 non-null float64\n",
      "dtypes: float64(9)\n",
      "memory usage: 3.8+ MB\n"
     ]
    }
   ],
   "source": [
    "quality_factors, t = factor_pipeline(QUALITY_FACTORS)\n",
    "print('Pipeline run time {:.2f} secs'.format(t))\n",
    "quality_factors.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Payout Factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PayoutFactors:\n",
    "\n",
    "    @staticmethod\n",
    "    def DividendPayoutRatio(mask):\n",
    "        \"\"\"Dividends Per Share divided by Earnings Per Share\"\"\"\n",
    "\n",
    "        dps = AnnualizedData(inputs = [Fundamentals.dividend_per_share_earnings_reports_asof_date,\n",
    "                                        Fundamentals.dividend_per_share_earnings_reports], mask=mask)  \n",
    "        \n",
    "        eps = AnnualizedData(inputs = [Fundamentals.basic_eps_earnings_reports_asof_date,\n",
    "                                        Fundamentals.basic_eps_earnings_reports], mask=mask)\n",
    "        return dps / eps\n",
    "    \n",
    "    @staticmethod\n",
    "    def DividendGrowth(**kwargs):\n",
    "        \"\"\"Annualized percentage DPS change\"\"\"        \n",
    "        return Fundamentals.dps_growth.latest    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "PAYOUT_FACTORS = {\n",
    "    'Dividend Payout Ratio': PayoutFactors.DividendPayoutRatio,\n",
    "    'Dividend Growth': PayoutFactors.DividendGrowth\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline run time 22.46 secs\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 50362 entries, (2014-01-02 00:00:00+00:00, Equity(24 [AAPL])) to (2015-12-31 00:00:00+00:00, Equity(47208 [GPRO]))\n",
      "Data columns (total 2 columns):\n",
      "Dividend Growth          40517 non-null float64\n",
      "Dividend Payout Ratio    39947 non-null float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "payout_factors, t = factor_pipeline(PAYOUT_FACTORS)\n",
    "print('Pipeline run time {:.2f} secs'.format(t))\n",
    "payout_factors.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Profitability Factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ProfitabilityFactors:\n",
    "    \n",
    "    @staticmethod\n",
    "    def GrossProfitMargin(mask):\n",
    "        \"\"\"Gross Profit divided by Net Sales\"\"\"\n",
    "\n",
    "        gross_profit = AnnualizedData([Fundamentals.gross_profit_asof_date,\n",
    "                              Fundamentals.gross_profit], mask=mask)  \n",
    "        sales = AnnualizedData([Fundamentals.total_revenue_asof_date,\n",
    "                                Fundamentals.total_revenue], mask=mask)\n",
    "        return gross_profit / sales   \n",
    "    \n",
    "    @staticmethod\n",
    "    def NetIncomeMargin(mask):\n",
    "        \"\"\"Net income divided by Net Sales\"\"\"\n",
    "\n",
    "        net_income = AnnualizedData([Fundamentals.net_income_income_statement_asof_date,\n",
    "                              Fundamentals.net_income_income_statement], mask=mask)  \n",
    "        sales = AnnualizedData([Fundamentals.total_revenue_asof_date,\n",
    "                                Fundamentals.total_revenue], mask=mask)\n",
    "        return net_income / sales   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "PROFITABIILTY_FACTORS = {\n",
    "    'Gross Profit Margin': ProfitabilityFactors.GrossProfitMargin,\n",
    "    'Net Income Margin': ProfitabilityFactors.NetIncomeMargin,\n",
    "    'Return on Equity': Fundamentals.roe.latest,\n",
    "    'Return on Assets': Fundamentals.roa.latest,\n",
    "    'Return on Invested Capital': Fundamentals.roic.latest\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline run time 22.56 secs\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 50362 entries, (2014-01-02 00:00:00+00:00, Equity(24 [AAPL])) to (2015-12-31 00:00:00+00:00, Equity(47208 [GPRO]))\n",
      "Data columns (total 2 columns):\n",
      "Dividend Growth          40517 non-null float64\n",
      "Dividend Payout Ratio    39947 non-null float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "profitability_factors, t = factor_pipeline(PAYOUT_FACTORS)\n",
    "print('Pipeline run time {:.2f} secs'.format(t))\n",
    "payout_factors.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# profitability_pipeline().show_graph(format='png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Returns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will test predictions for various lookahead periods to identify the best holding periods that generate the best predictability, measured by the information coefficient. \n",
    "\n",
    "More specifically, we compute returns for 1, 5, 10, and 20 days using the built-in Returns function, resulting in over 50,000 observations for the universe of 100 stocks over two years (that include approximately 252 trading days each)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 50362 entries, (2014-01-02 00:00:00+00:00, Equity(24 [AAPL])) to (2015-12-31 00:00:00+00:00, Equity(47208 [GPRO]))\n",
      "Data columns (total 4 columns):\n",
      "Returns10D    50362 non-null float64\n",
      "Returns1D     50362 non-null float64\n",
      "Returns20D    50360 non-null float64\n",
      "Returns5D     50362 non-null float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 1.9+ MB\n"
     ]
    }
   ],
   "source": [
    "lookahead = [1, 5, 10, 20]\n",
    "returns = run_pipeline(Pipeline({'Returns{}D'.format(i): Returns(inputs=[USEquityPricing.close], \n",
    "                                          window_length=i+1, mask=UNIVERSE) for i in lookahead},\n",
    "                                screen=UNIVERSE),\n",
    "                       start_date=START, \n",
    "                       end_date=END)\n",
    "return_cols = ['Returns{}D'.format(i) for i in lookahead]\n",
    "returns.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use over 50 features that cover a broad range of factors based on market, fundamental, and alternative data. The notebook also includes custom transformations to convert fundamental data that is typically available in quarterly reporting frequency to rolling annual totals or averages to avoid excessive season fluctuations.\n",
    "\n",
    "Once the factors have been computed through the various pipelines outlined in Chapter 4, Alpha Factors – Research and Evaluation, we combine them using pd.concat(), assign index names, and create a categorical variable that identifies the asset for each data point:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.concat([returns,\n",
    "                 value_factors,\n",
    "                 momentum_factors,\n",
    "                 quality_factors,\n",
    "                 payout_factors,\n",
    "                 growth_factors,\n",
    "                 efficiency_factors,\n",
    "                 risk_factors], axis=1).sortlevel()\n",
    "data.index.names = ['date', 'asset']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['stock'] = data.index.get_level_values('asset').map(lambda x: x.asset_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove columns and rows with less than 80% of data availability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In a next step, we remove rows and columns that lack more than 20 percent of the observations, resulting in a loss of six percent of the observations and three columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2,985 rows and 3 columns dropped\n"
     ]
    }
   ],
   "source": [
    "rows_before, cols_before = data.shape\n",
    "data = (data\n",
    "        .dropna(axis=1, thresh=int(len(data)*.8))\n",
    "        .dropna(thresh=int(len(data.columns) * .8)))\n",
    "data = data.fillna(data.median())\n",
    "rows_after, cols_after = data.shape\n",
    "print('{:,d} rows and {:,d} columns dropped'.format(rows_before-rows_after, cols_before-cols_after))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point, we have 51 features and the categorical identifier of the stock:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 47377 entries, (2014-01-02 00:00:00+00:00, Equity(24 [AAPL])) to (2015-12-31 00:00:00+00:00, Equity(47208 [GPRO]))\n",
      "Data columns (total 52 columns):\n",
      "AssetToEquityRatio             47377 non-null float64\n",
      "AssetTurnover                  47377 non-null float64\n",
      "CFO To Assets                  47377 non-null float64\n",
      "Capex To Assets                47377 non-null float64\n",
      "Capex To FCF                   47377 non-null float64\n",
      "Capex To Sales                 47377 non-null float64\n",
      "CurrentRatio                   47377 non-null float64\n",
      "DebtToAssetRatio               47377 non-null float64\n",
      "DebtToEquityRatio              47377 non-null float64\n",
      "Directional Movement Index     47377 non-null float64\n",
      "Dividend Growth                47377 non-null float64\n",
      "DividendYield                  47377 non-null float64\n",
      "Downside Risk                  47377 non-null float64\n",
      "EBIT To Assets                 47377 non-null float64\n",
      "EBITDAYield                    47377 non-null float64\n",
      "EPS                            47377 non-null float64\n",
      "EPS Growth 12M                 47377 non-null float64\n",
      "EPS Growth 3M                  47377 non-null float64\n",
      "EVToEBITDA                     47377 non-null float64\n",
      "EVToFCF                        47377 non-null float64\n",
      "Index Beta                     47377 non-null float64\n",
      "Log Market Cap                 47377 non-null float64\n",
      "MertonsDD                      47377 non-null float64\n",
      "Money Flow Index               47377 non-null float64\n",
      "Net Debt                       47377 non-null float64\n",
      "Net Debt Growth 12M            47377 non-null float64\n",
      "Net Debt Growth 3M             47377 non-null float64\n",
      "Percent Above Low              47377 non-null float64\n",
      "Percent Below High             47377 non-null float64\n",
      "Price Oscillator               47377 non-null float64\n",
      "PriceToBook                    47377 non-null float64\n",
      "PriceToDilutedEarningsTTM      47377 non-null float64\n",
      "PriceToEarningsTTM             47377 non-null float64\n",
      "PriceToFCF                     47377 non-null float64\n",
      "PriceToOperatingCashflow       47377 non-null float64\n",
      "PriceToSalesTTM                47377 non-null float64\n",
      "Retained Earnings To Assets    47377 non-null float64\n",
      "Returns10D                     47377 non-null float64\n",
      "Returns1D                      47377 non-null float64\n",
      "Returns20D                     47377 non-null float64\n",
      "Returns5D                      47377 non-null float64\n",
      "Sales                          47377 non-null float64\n",
      "Sales Growth 12M               47377 non-null float64\n",
      "Sales Growth 3M                47377 non-null float64\n",
      "Total Assets                   47377 non-null float64\n",
      "Total Assets Growth 12M        47377 non-null float64\n",
      "Total Assets Growth 3M         47377 non-null float64\n",
      "Trendline                      47377 non-null float64\n",
      "Volatility 3M                  47377 non-null float64\n",
      "WorkingCapitalToAssets         47377 non-null float64\n",
      "WorkingCapitalToSales          47377 non-null float64\n",
      "stock                          47377 non-null object\n",
      "dtypes: float64(51), object(1)\n",
      "memory usage: 19.2+ MB\n"
     ]
    }
   ],
   "source": [
    "data.sort_index(1).info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For linear regression models, it is important to explore the correlation among the features to identify multicollinearity issues, and to check the correlation between the features and the target. The notebook contains a seaborn clustermap that shows the hierarchical structure of the feature correlation matrix. It identifies a small number of highly correlated clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = sns.clustermap(data.drop(['stock'] + return_cols, axis=1).corr())\n",
    "plt.gcf().set_size_inches((14,14));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dummy encoding of categorical variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to convert the categorical stock variable into a numeric format so that the linear regression can process it. For this purpose, we use dummy encoding that creates individual columns for each category level and flags the presence of this level in the original categorical column with an entry of 1, and 0 otherwise. The pandas function get_dummies() automates dummy encoding. It detects and properly converts columns of type objects as illustrated next. If you need dummy variables for columns containing integers, for instance, you can identify them using the keyword columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 47377 entries, (2014-01-02 00:00:00+00:00, Equity(24 [AAPL])) to (2015-12-31 00:00:00+00:00, Equity(47208 [GPRO]))\n",
      "Columns: 182 entries, DividendYield to stock_YELP INC\n",
      "dtypes: float64(182)\n",
      "memory usage: 66.1+ MB\n"
     ]
    }
   ],
   "source": [
    "X = pd.get_dummies(data.drop(return_cols, axis=1))\n",
    "X.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating forward returns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal is to predict returns over a given holding period. Hence, we need to align the features with return values with the corresponding return data point 1, 5, 10, or 20 days into the future for each equity. We achieve this by combining the pandas .groupby() method with the .shift() method as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 47377 entries, (2014-01-02 00:00:00+00:00, Equity(24 [AAPL])) to (2015-12-31 00:00:00+00:00, Equity(47208 [GPRO]))\n",
      "Data columns (total 4 columns):\n",
      "Returns1D     47242 non-null float64\n",
      "Returns5D     46706 non-null float64\n",
      "Returns10D    46036 non-null float64\n",
      "Returns20D    44696 non-null float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 1.8+ MB\n"
     ]
    }
   ],
   "source": [
    "y = data.loc[:, return_cols]\n",
    "shifted_y = []\n",
    "for col in y.columns:\n",
    "    t = int(re.search(r'\\d+', col).group(0))\n",
    "    shifted_y.append(y.groupby(level='asset')['Returns{}D'.format(t)].shift(-t).to_frame(col))\n",
    "y = pd.concat(shifted_y, axis=1)\n",
    "y.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/seaborn/categorical.py:2171: UserWarning: The boxplot API has been changed. Attempting to adjust your arguments for the new API (which might not work). Please update your code. See the version 0.6 release notes for more info.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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Zlpw6jBw5stbtxYsXm9QJGsNvsJAkdevWzbAGAAAId3l5eYY1rIVgA0mSw+Ew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\nwQYAAACA7f0/9mHOqH2g4isAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcdbb1cc9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(y[return_cols])\n",
    "ax.set_title('Return Distriubtions');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statsmodels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can estimate a linear regression model using OLS with statsmodels as demonstrated previously. We select a forward return, for example for a 10-day holding period, remove outliers below the 2.5% and above the 97.5% percentiles, and fit the model accordingly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>Returns1D</td>    <th>  R-squared:         </th>  <td>   0.004</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   1.084</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 09 Sep 2018</td> <th>  Prob (F-statistic):</th>   <td> 0.214</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:17:58</td>     <th>  Log-Likelihood:    </th> <td>1.3306e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 44878</td>      <th>  AIC:               </th> <td>-2.658e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 44703</td>      <th>  BIC:               </th> <td>-2.642e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>   174</td>      <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                       <td></td>                          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>DividendYield</th>                               <td> 1.249e-06</td> <td>  4.7e-07</td> <td>    2.659</td> <td> 0.008</td> <td> 3.28e-07  2.17e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>EBITDAYield</th>                                 <td>-5.537e-06</td> <td> 9.48e-06</td> <td>   -0.584</td> <td> 0.559</td> <td>-2.41e-05   1.3e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>EVToEBITDA</th>                                  <td>  4.87e-06</td> <td>  8.7e-06</td> <td>    0.560</td> <td> 0.575</td> <td>-1.22e-05  2.19e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>EVToFCF</th>                                     <td> 3.621e-06</td> <td> 1.08e-05</td> <td>    0.334</td> <td> 0.738</td> <td>-1.76e-05  2.48e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PriceToBook</th>                                 <td>-5.411e-07</td> <td> 1.03e-05</td> <td>   -0.052</td> <td> 0.958</td> <td>-2.08e-05  1.97e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PriceToDilutedEarningsTTM</th>                   <td> 4.561e-06</td> <td> 4.64e-06</td> <td>    0.983</td> <td> 0.326</td> <td>-4.54e-06  1.37e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PriceToEarningsTTM</th>                          <td>     6e-08</td> <td> 2.05e-07</td> <td>    0.293</td> <td> 0.770</td> <td>-3.41e-07  4.61e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PriceToFCF</th>                                  <td>-9.612e-07</td> <td> 9.07e-06</td> <td>   -0.106</td> <td> 0.916</td> <td>-1.87e-05  1.68e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PriceToOperatingCashflow</th>                    <td>-6.258e-07</td> <td> 6.16e-06</td> <td>   -0.102</td> <td> 0.919</td> <td>-1.27e-05  1.14e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PriceToSalesTTM</th>                             <td>-2.088e-06</td> <td> 5.16e-07</td> <td>   -4.048</td> <td> 0.000</td> <td> -3.1e-06 -1.08e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Directional Movement Index</th>                  <td>-3.169e-07</td> <td> 2.09e-06</td> <td>   -0.152</td> <td> 0.879</td> <td> -4.4e-06  3.77e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Money Flow Index</th>                            <td>-2.768e-06</td> <td>  2.7e-06</td> <td>   -1.025</td> <td> 0.305</td> <td>-8.06e-06  2.52e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Percent Above Low</th>                           <td> 2.875e-06</td> <td> 5.17e-06</td> <td>    0.556</td> <td> 0.578</td> <td>-7.26e-06   1.3e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Percent Below High</th>                          <td>-1.786e-06</td> <td> 4.14e-06</td> <td>   -0.431</td> <td> 0.666</td> <td> -9.9e-06  6.33e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Price Oscillator</th>                            <td>  9.18e-07</td> <td> 2.92e-06</td> <td>    0.314</td> <td> 0.753</td> <td>-4.81e-06  6.64e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Trendline</th>                                   <td> 2.007e-06</td> <td> 4.41e-06</td> <td>    0.455</td> <td> 0.649</td> <td>-6.64e-06  1.07e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AssetToEquityRatio</th>                          <td> 5.633e-07</td> <td> 3.98e-07</td> <td>    1.414</td> <td> 0.157</td> <td>-2.17e-07  1.34e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AssetTurnover</th>                               <td> -1.27e-05</td> <td> 1.74e-05</td> <td>   -0.731</td> <td> 0.465</td> <td>-4.68e-05  2.14e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CurrentRatio</th>                                <td> 3.246e-07</td> <td>  5.2e-07</td> <td>    0.625</td> <td> 0.532</td> <td>-6.94e-07  1.34e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>DebtToAssetRatio</th>                            <td> 5.697e-07</td> <td> 3.18e-07</td> <td>    1.792</td> <td> 0.073</td> <td>-5.35e-08  1.19e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>DebtToEquityRatio</th>                           <td>-5.451e-07</td> <td> 3.69e-07</td> <td>   -1.476</td> <td> 0.140</td> <td>-1.27e-06  1.79e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MertonsDD</th>                                   <td>   -0.0001</td> <td> 4.64e-05</td> <td>   -2.661</td> <td> 0.008</td> <td>   -0.000 -3.25e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WorkingCapitalToAssets</th>                      <td> 1.452e-07</td> <td> 6.31e-07</td> <td>    0.230</td> <td> 0.818</td> <td>-1.09e-06  1.38e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WorkingCapitalToSales</th>                       <td> -2.24e-05</td> <td> 2.03e-05</td> <td>   -1.106</td> <td> 0.269</td> <td>-6.21e-05  1.73e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dividend Growth</th>                             <td>-1.505e-08</td> <td> 1.96e-07</td> <td>   -0.077</td> <td> 0.939</td> <td>   -4e-07  3.69e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>EPS</th>                                         <td>-1.219e-07</td> <td>  3.1e-07</td> <td>   -0.393</td> <td> 0.694</td> <td> -7.3e-07  4.86e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Net Debt</th>                                    <td>-1.968e-14</td> <td> 1.12e-14</td> <td>   -1.760</td> <td> 0.078</td> <td>-4.16e-14  2.23e-15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sales</th>                                       <td>  5.85e-15</td> <td> 1.04e-14</td> <td>    0.563</td> <td> 0.574</td> <td>-1.45e-14  2.62e-14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Total Assets</th>                                <td>-1.665e-14</td> <td> 7.87e-15</td> <td>   -2.116</td> <td> 0.034</td> <td>-3.21e-14 -1.23e-15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>EPS Growth 3M</th>                               <td>-5.625e-08</td> <td> 4.75e-07</td> <td>   -0.118</td> <td> 0.906</td> <td>-9.87e-07  8.75e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>EPS Growth 12M</th>                              <td> 6.686e-07</td> <td> 4.76e-07</td> <td>    1.405</td> <td> 0.160</td> <td>-2.64e-07   1.6e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Net Debt Growth 3M</th>                          <td> 8.433e-08</td> <td> 6.03e-07</td> <td>    0.140</td> <td> 0.889</td> <td> -1.1e-06  1.27e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Net Debt Growth 12M</th>                         <td> 4.558e-07</td> <td> 6.14e-07</td> <td>    0.743</td> <td> 0.458</td> <td>-7.47e-07  1.66e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sales Growth 3M</th>                             <td> 2.469e-07</td> <td>  6.4e-07</td> <td>    0.386</td> <td> 0.699</td> <td>-1.01e-06   1.5e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sales Growth 12M</th>                            <td>-4.861e-07</td> <td> 6.53e-07</td> <td>   -0.745</td> <td> 0.456</td> <td>-1.77e-06  7.93e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Total Assets Growth 3M</th>                      <td> 4.535e-07</td> <td> 7.16e-07</td> <td>    0.633</td> <td> 0.527</td> <td> -9.5e-07  1.86e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Total Assets Growth 12M</th>                     <td>-3.477e-07</td> <td> 7.64e-07</td> <td>   -0.455</td> <td> 0.649</td> <td>-1.84e-06  1.15e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CFO To Assets</th>                               <td> 1.731e-05</td> <td> 8.66e-06</td> <td>    1.999</td> <td> 0.046</td> <td> 3.36e-07  3.43e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Capex To Assets</th>                             <td>-2.097e-05</td> <td> 1.87e-05</td> <td>   -1.124</td> <td> 0.261</td> <td>-5.75e-05  1.56e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Capex To FCF</th>                                <td>  1.29e-06</td> <td> 1.05e-05</td> <td>    0.123</td> <td> 0.902</td> <td>-1.92e-05  2.18e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Capex To Sales</th>                              <td> 1.637e-05</td> <td>    2e-05</td> <td>    0.820</td> <td> 0.412</td> <td>-2.28e-05  5.55e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>EBIT To Assets</th>                              <td> 7.612e-06</td> <td> 8.75e-06</td> <td>    0.870</td> <td> 0.385</td> <td>-9.55e-06  2.48e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Retained Earnings To Assets</th>                 <td>-3.547e-05</td> <td> 1.84e-05</td> <td>   -1.923</td> <td> 0.054</td> <td>-7.16e-05  6.76e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Downside Risk</th>                               <td> 4.943e-07</td> <td> 5.32e-06</td> <td>    0.093</td> <td> 0.926</td> <td>-9.93e-06  1.09e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Index Beta</th>                                  <td>-8.585e-08</td> <td> 1.09e-07</td> <td>   -0.786</td> <td> 0.432</td> <td>   -3e-07  1.28e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Log Market Cap</th>                              <td> 1.847e-05</td> <td> 1.86e-05</td> <td>    0.995</td> <td> 0.320</td> <td>-1.79e-05  5.49e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Volatility 3M</th>                               <td>-8.138e-06</td> <td> 4.53e-06</td> <td>   -1.798</td> <td> 0.072</td> <td> -1.7e-05  7.33e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_3D SYSTEMS CORP</th>                       <td>    0.0157</td> <td>    0.004</td> <td>    3.600</td> <td> 0.000</td> <td>    0.007     0.024</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_3M COMPANY</th>                            <td>    0.0108</td> <td>    0.004</td> <td>    2.823</td> <td> 0.005</td> <td>    0.003     0.018</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_ABBOTT LABORATORIES</th>                   <td>    0.0069</td> <td>    0.003</td> <td>    2.315</td> <td> 0.021</td> <td>    0.001     0.013</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_ABBVIE INC</th>                            <td>    0.0163</td> <td>    0.006</td> <td>    2.916</td> <td> 0.004</td> <td>    0.005     0.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_ALLERGAN INC</th>                          <td>    0.0103</td> <td>    0.003</td> <td>    3.563</td> <td> 0.000</td> <td>    0.005     0.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_ALLERGAN PLC</th>                          <td>    0.0135</td> <td>    0.004</td> <td>    3.310</td> <td> 0.001</td> <td>    0.006     0.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_ALTABA INC</th>                            <td>    0.0161</td> <td>    0.005</td> <td>    3.560</td> <td> 0.000</td> <td>    0.007     0.025</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_ALTRIA GROUP INC.</th>                     <td>    0.0111</td> <td>    0.004</td> <td>    2.847</td> <td> 0.004</td> <td>    0.003     0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_AMAZON.COM INC</th>                        <td>    0.0123</td> <td>    0.005</td> <td>    2.683</td> <td> 0.007</td> <td>    0.003     0.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_AMERICAN AIRLINES GROUP INC</th>           <td>    0.0143</td> <td>    0.005</td> <td>    2.720</td> <td> 0.007</td> <td>    0.004     0.025</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_AMERICAN EXPRESS COMPANY</th>              <td>    0.0065</td> <td>    0.002</td> <td>    2.656</td> <td> 0.008</td> <td>    0.002     0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_AMERICAN INTL GROUP INC</th>               <td>    0.0118</td> <td>    0.004</td> <td>    2.850</td> <td> 0.004</td> <td>    0.004     0.020</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_AMGEN INC</th>                             <td>    0.0064</td> <td>    0.003</td> <td>    2.421</td> <td> 0.015</td> <td>    0.001     0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_ANADARKO PETROLEUM CORP</th>               <td>    0.0077</td> <td>    0.002</td> <td>    3.231</td> <td> 0.001</td> <td>    0.003     0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_APACHE CORP</th>                           <td>    0.0045</td> <td>    0.004</td> <td>    1.262</td> <td> 0.207</td> <td>   -0.003     0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_APPLE INC</th>                             <td>    0.0086</td> <td>    0.004</td> <td>    2.278</td> <td> 0.023</td> <td>    0.001     0.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_APPLIED MATERIALS INC</th>                 <td>    0.0075</td> <td>    0.003</td> <td>    2.572</td> <td> 0.010</td> <td>    0.002     0.013</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_ARCONIC INC</th>                           <td>    0.0026</td> <td>    0.002</td> <td>    1.210</td> <td> 0.226</td> <td>   -0.002     0.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_AT&T INC. COM</th>                         <td>    0.0103</td> <td>    0.004</td> <td>    2.420</td> <td> 0.016</td> <td>    0.002     0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_Alphabet Inc. Cl A</th>                    <td>    0.0192</td> <td>    0.005</td> <td>    3.528</td> <td> 0.000</td> <td>    0.009     0.030</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_BAKER HUGHES INC</th>                      <td>    0.0078</td> <td>    0.003</td> <td>    2.946</td> <td> 0.003</td> <td>    0.003     0.013</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_BANK OF AMERICA CORP</th>                  <td>    0.0404</td> <td>    0.016</td> <td>    2.532</td> <td> 0.011</td> <td>    0.009     0.072</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_BERKSHIRE HATHAWAY INC CL-B</th>           <td>    0.0171</td> <td>    0.006</td> <td>    3.069</td> <td> 0.002</td> <td>    0.006     0.028</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_BIOGEN INC</th>                            <td>    0.0120</td> <td>    0.004</td> <td>    3.275</td> <td> 0.001</td> <td>    0.005     0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_BOEING CO</th>                             <td>    0.0053</td> <td>    0.003</td> <td>    1.864</td> <td> 0.062</td> <td>   -0.000     0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_BOOKING HOLDINGS INC</th>                  <td>    0.0153</td> <td>    0.005</td> <td>    3.232</td> <td> 0.001</td> <td>    0.006     0.025</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_BRISTOL MYERS SQUIBB COMPANY</th>          <td>    0.0097</td> <td>    0.003</td> <td>    3.001</td> <td> 0.003</td> <td>    0.003     0.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_BROADCOM CORP</th>                         <td>    0.0134</td> <td>    0.004</td> <td>    3.087</td> <td> 0.002</td> <td>    0.005     0.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_BROADCOM INC</th>                          <td>    0.0173</td> <td>    0.006</td> <td>    3.112</td> <td> 0.002</td> <td>    0.006     0.028</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_CATERPILLAR INC</th>                       <td>    0.0042</td> <td>    0.003</td> <td>    1.502</td> <td> 0.133</td> <td>   -0.001     0.010</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_CELGENE CORP</th>                          <td>    0.0091</td> <td>    0.003</td> <td>    2.905</td> <td> 0.004</td> <td>    0.003     0.015</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_CHESAPEAKE ENERGY CORP</th>                <td>    0.0041</td> <td>    0.005</td> <td>    0.874</td> <td> 0.382</td> <td>   -0.005     0.013</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_CHEVRON CORPORATION</th>                   <td>    0.0137</td> <td>    0.006</td> <td>    2.386</td> <td> 0.017</td> <td>    0.002     0.025</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_CISCO SYSTEMS INC</th>                     <td>    0.0066</td> <td>    0.003</td> <td>    2.363</td> <td> 0.018</td> <td>    0.001     0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_CITIGROUP</th>                             <td>    0.0371</td> <td>    0.014</td> <td>    2.713</td> <td> 0.007</td> <td>    0.010     0.064</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_COCA-COLA CO</th>                          <td>    0.0101</td> <td>    0.004</td> <td>    2.629</td> <td> 0.009</td> <td>    0.003     0.018</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_COMCAST CORP</th>                          <td>    0.0081</td> <td>    0.003</td> <td>    2.917</td> <td> 0.004</td> <td>    0.003     0.013</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_CONOCOPHILLIPS</th>                        <td>    0.0125</td> <td>    0.005</td> <td>    2.566</td> <td> 0.010</td> <td>    0.003     0.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_COVIDIEN PLC</th>                          <td>    0.0196</td> <td>    0.005</td> <td>    3.839</td> <td> 0.000</td> <td>    0.010     0.030</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_CVS HEALTH CORP</th>                       <td>    0.0077</td> <td>    0.004</td> <td>    2.122</td> <td> 0.034</td> <td>    0.001     0.015</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_DEERE & CO</th>                            <td>    0.0050</td> <td>    0.003</td> <td>    1.671</td> <td> 0.095</td> <td>   -0.001     0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_DELTA AIR LINES INC</th>                   <td>    0.0141</td> <td>    0.005</td> <td>    2.882</td> <td> 0.004</td> <td>    0.005     0.024</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_DIRECTV</th>                               <td>    0.0114</td> <td>    0.005</td> <td>    2.405</td> <td> 0.016</td> <td>    0.002     0.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_DOLLAR GENERAL CORP</th>                   <td>    0.0143</td> <td>    0.005</td> <td>    2.899</td> <td> 0.004</td> <td>    0.005     0.024</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_DOW CHEMICAL CO</th>                       <td>    0.0055</td> <td>    0.003</td> <td>    1.960</td> <td> 0.050</td> <td>-5.93e-07     0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_E.I. Du Pont De Nemours A</th>             <td>    0.0060</td> <td>    0.003</td> <td>    2.109</td> <td> 0.035</td> <td>    0.000     0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_EBAY INC</th>                              <td>    0.0166</td> <td>    0.005</td> <td>    3.281</td> <td> 0.001</td> <td>    0.007     0.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_EMC CORPORATION</th>                       <td>    0.0082</td> <td>    0.003</td> <td>    2.886</td> <td> 0.004</td> <td>    0.003     0.014</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_EOG RESOURCES INC</th>                     <td>    0.0099</td> <td>    0.003</td> <td>    3.577</td> <td> 0.000</td> <td>    0.004     0.015</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_EXPRESS SCRIPTS HOLDING CO</th>            <td>    0.0034</td> <td>    0.003</td> <td>    1.166</td> <td> 0.244</td> <td>   -0.002     0.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_EXXON MOBIL CORPORATION</th>               <td>    0.0128</td> <td>    0.007</td> <td>    1.936</td> <td> 0.053</td> <td>   -0.000     0.026</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_FACEBOOK INC</th>                          <td>    0.0199</td> <td>    0.006</td> <td>    3.452</td> <td> 0.001</td> <td>    0.009     0.031</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_FEDEX CORPORATION</th>                     <td>    0.0079</td> <td>    0.003</td> <td>    2.618</td> <td> 0.009</td> <td>    0.002     0.014</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_FIRST SOLAR INC</th>                       <td>    0.0180</td> <td>    0.005</td> <td>    3.411</td> <td> 0.001</td> <td>    0.008     0.028</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_FORD MOTOR CO(NEW)</th>                    <td>    0.0057</td> <td>    0.004</td> <td>    1.626</td> <td> 0.104</td> <td>   -0.001     0.013</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_FREEPORT-MCMORAN INC</th>                  <td>    0.0090</td> <td>    0.004</td> <td>    2.333</td> <td> 0.020</td> <td>    0.001     0.017</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_GENERAL ELECTRIC CO</th>                   <td>    0.0165</td> <td>    0.005</td> <td>    3.025</td> <td> 0.002</td> <td>    0.006     0.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_GENERAL MOTORS CO</th>                     <td>    0.0131</td> <td>    0.006</td> <td>    2.358</td> <td> 0.018</td> <td>    0.002     0.024</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_GILEAD SCIENCES INC</th>                   <td>    0.0108</td> <td>    0.003</td> <td>    3.141</td> <td> 0.002</td> <td>    0.004     0.017</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_GOLDMAN SACHS GROUP INC</th>               <td>    0.0317</td> <td>    0.007</td> <td>    4.257</td> <td> 0.000</td> <td>    0.017     0.046</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_GOPRO INC</th>                             <td>    0.0199</td> <td>    0.006</td> <td>    3.550</td> <td> 0.000</td> <td>    0.009     0.031</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_HALLIBURTON CO (HOLDING CO)</th>           <td>    0.0084</td> <td>    0.003</td> <td>    2.752</td> <td> 0.006</td> <td>    0.002     0.014</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_HOME DEPOT INC</th>                        <td>    0.0070</td> <td>    0.003</td> <td>    2.052</td> <td> 0.040</td> <td>    0.000     0.014</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_HP INC</th>                                <td>    0.0051</td> <td>    0.003</td> <td>    1.624</td> <td> 0.104</td> <td>   -0.001     0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_INTEL CORP</th>                            <td>    0.0087</td> <td>    0.003</td> <td>    2.535</td> <td> 0.011</td> <td>    0.002     0.015</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_INTL BUSINESS MACHINES CORP</th>           <td>    0.0087</td> <td>    0.004</td> <td>    2.280</td> <td> 0.023</td> <td>    0.001     0.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_JOHNSON AND JOHNSON</th>                   <td>    0.0111</td> <td>    0.004</td> <td>    2.834</td> <td> 0.005</td> <td>    0.003     0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_JPMORGAN CHASE & CO COM STK</th>           <td>    0.0545</td> <td>    0.019</td> <td>    2.818</td> <td> 0.005</td> <td>    0.017     0.092</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_KEURIG GREEN MOUNTAIN INC</th>             <td>    0.0146</td> <td>    0.004</td> <td>    3.591</td> <td> 0.000</td> <td>    0.007     0.023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_KINDER MORGAN INC</th>                     <td>    0.0132</td> <td>    0.005</td> <td>    2.470</td> <td> 0.014</td> <td>    0.003     0.024</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_LAS VEGAS SANDS CORP</th>                  <td>    0.0127</td> <td>    0.005</td> <td>    2.488</td> <td> 0.013</td> <td>    0.003     0.023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_LILLY ELI & CO</th>                        <td>    0.0102</td> <td>    0.004</td> <td>    2.885</td> <td> 0.004</td> <td>    0.003     0.017</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_LINKEDIN CORP</th>                         <td>    0.0197</td> <td>    0.006</td> <td>    3.505</td> <td> 0.000</td> <td>    0.009     0.031</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_LOWES COMPANIES INC</th>                   <td>    0.0075</td> <td>    0.003</td> <td>    2.483</td> <td> 0.013</td> <td>    0.002     0.013</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_LYONDELLBASELL INDUSTRIES NV</th>          <td>    0.0135</td> <td>    0.005</td> <td>    2.593</td> <td> 0.010</td> <td>    0.003     0.024</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MARATHON PETROLEUM CORP</th>               <td>    0.0128</td> <td>    0.006</td> <td>    2.268</td> <td> 0.023</td> <td>    0.002     0.024</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MASTERCARD INCORPORATED</th>               <td>    0.0206</td> <td>    0.006</td> <td>    3.731</td> <td> 0.000</td> <td>    0.010     0.031</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MCDONALDS CORP</th>                        <td>    0.0099</td> <td>    0.004</td> <td>    2.513</td> <td> 0.012</td> <td>    0.002     0.018</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MEDTRONIC PLC</th>                         <td>    0.0119</td> <td>    0.004</td> <td>    3.390</td> <td> 0.001</td> <td>    0.005     0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MERCK & CO INC</th>                        <td>    0.0110</td> <td>    0.004</td> <td>    2.966</td> <td> 0.003</td> <td>    0.004     0.018</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_METLIFE  INC</th>                          <td>    0.0260</td> <td>    0.008</td> <td>    3.353</td> <td> 0.001</td> <td>    0.011     0.041</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MICHAEL KORS HOLDINGS LTD</th>             <td>    0.0214</td> <td>    0.006</td> <td>    3.646</td> <td> 0.000</td> <td>    0.010     0.033</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MICRON TECHNOLOGY INC</th>                 <td>    0.0102</td> <td>    0.003</td> <td>    3.287</td> <td> 0.001</td> <td>    0.004     0.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MICROSOFT CORP</th>                        <td>    0.0115</td> <td>    0.004</td> <td>    3.028</td> <td> 0.002</td> <td>    0.004     0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MONDELEZ INTERNATIONAL INC</th>            <td>    0.0133</td> <td>    0.005</td> <td>    2.875</td> <td> 0.004</td> <td>    0.004     0.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MONSANTO COMPANY</th>                      <td>    0.0153</td> <td>    0.005</td> <td>    3.201</td> <td> 0.001</td> <td>    0.006     0.025</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MORGAN STANLEY</th>                        <td>    0.0273</td> <td>    0.007</td> <td>    3.884</td> <td> 0.000</td> <td>    0.014     0.041</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MYLAN NV</th>                              <td>    0.0111</td> <td>    0.003</td> <td>    3.260</td> <td> 0.001</td> <td>    0.004     0.018</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_NATIONAL OILWELL VARCO  INC.</th>          <td>    0.0131</td> <td>    0.005</td> <td>    2.642</td> <td> 0.008</td> <td>    0.003     0.023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_NETFLIX INC</th>                           <td>    0.0179</td> <td>    0.005</td> <td>    3.555</td> <td> 0.000</td> <td>    0.008     0.028</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_NEWMONT MINING CORP (HOLDING COMPANY)</th> <td>    0.0098</td> <td>    0.004</td> <td>    2.773</td> <td> 0.006</td> <td>    0.003     0.017</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_NEWS CP - CL A</th>                        <td>    0.0129</td> <td>    0.004</td> <td>    3.348</td> <td> 0.001</td> <td>    0.005     0.020</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_NIKE INC CL-B</th>                         <td>    0.0104</td> <td>    0.004</td> <td>    2.884</td> <td> 0.004</td> <td>    0.003     0.018</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_OCCIDENTAL PETROLEUM CORP</th>             <td>    0.0118</td> <td>    0.004</td> <td>    3.153</td> <td> 0.002</td> <td>    0.004     0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_ORACLE CORP</th>                           <td>    0.0118</td> <td>    0.004</td> <td>    3.297</td> <td> 0.001</td> <td>    0.005     0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_PANDORA MEDIA INC</th>                     <td>    0.0235</td> <td>    0.006</td> <td>    3.942</td> <td> 0.000</td> <td>    0.012     0.035</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_PENNEY J.C. CO INC (HOLDING COMPANY)</th>  <td>    0.0027</td> <td>    0.003</td> <td>    0.905</td> <td> 0.365</td> <td>   -0.003     0.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_PEPSICO INC</th>                           <td>    0.0104</td> <td>    0.004</td> <td>    2.533</td> <td> 0.011</td> <td>    0.002     0.018</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_PFIZER INC</th>                            <td>    0.0134</td> <td>    0.004</td> <td>    3.287</td> <td> 0.001</td> <td>    0.005     0.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_PHILIP MORRIS INTERNATIONAL INC</th>       <td>    0.0154</td> <td>    0.006</td> <td>    2.576</td> <td> 0.010</td> <td>    0.004     0.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_PIONEER NAT RES CO</th>                    <td>    0.0177</td> <td>    0.004</td> <td>    4.022</td> <td> 0.000</td> <td>    0.009     0.026</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_PRECISION CASTPARTS CORP</th>              <td>    0.0145</td> <td>    0.004</td> <td>    3.574</td> <td> 0.000</td> <td>    0.007     0.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_PROCTER & GAMBLE CO</th>                   <td>    0.0115</td> <td>    0.004</td> <td>    2.722</td> <td> 0.006</td> <td>    0.003     0.020</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_QUALCOMM INC</th>                          <td>    0.0131</td> <td>    0.004</td> <td>    3.223</td> <td> 0.001</td> <td>    0.005     0.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_REGENERON PHARMACEUTICALS INC</th>         <td>    0.0117</td> <td>    0.005</td> <td>    2.380</td> <td> 0.017</td> <td>    0.002     0.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_SALESFORCE.COM INC</th>                    <td>    0.0173</td> <td>    0.005</td> <td>    3.443</td> <td> 0.001</td> <td>    0.007     0.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_SALIX PHARMACEUTICALS LTD</th>             <td>-2.111e-18</td> <td> 7.34e-17</td> <td>   -0.029</td> <td> 0.977</td> <td>-1.46e-16  1.42e-16</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_SANDISK CORP</th>                          <td>    0.0154</td> <td>    0.004</td> <td>    3.629</td> <td> 0.000</td> <td>    0.007     0.024</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_SCHLUMBERGER LTD.</th>                     <td>    0.0120</td> <td>    0.004</td> <td>    3.003</td> <td> 0.003</td> <td>    0.004     0.020</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_SKYWORKS SOLUTIONS INC</th>                <td>    0.0191</td> <td>    0.005</td> <td>    3.796</td> <td> 0.000</td> <td>    0.009     0.029</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_SOLARCITY CORP</th>                        <td>    0.0208</td> <td>    0.006</td> <td>    3.549</td> <td> 0.000</td> <td>    0.009     0.032</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_SOUTHWEST AIRLINES CO</th>                 <td>    0.0092</td> <td>    0.003</td> <td>    2.885</td> <td> 0.004</td> <td>    0.003     0.015</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_STARBUCKS CORPORATION</th>                 <td>    0.0147</td> <td>    0.004</td> <td>    3.658</td> <td> 0.000</td> <td>    0.007     0.023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_SUNEDISON INC</th>                         <td>    0.0183</td> <td>    0.006</td> <td>    3.237</td> <td> 0.001</td> <td>    0.007     0.029</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_TARGET CORPORATION</th>                    <td>    0.0104</td> <td>    0.004</td> <td>    2.349</td> <td> 0.019</td> <td>    0.002     0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_TESLA INC</th>                             <td>    0.0196</td> <td>    0.006</td> <td>    3.520</td> <td> 0.000</td> <td>    0.009     0.031</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_TEXAS INSTRUMENTS INC</th>                 <td>    0.0141</td> <td>    0.004</td> <td>    3.229</td> <td> 0.001</td> <td>    0.006     0.023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_TIME WARNER CABLE INC</th>                 <td>    0.0140</td> <td>    0.005</td> <td>    2.883</td> <td> 0.004</td> <td>    0.004     0.024</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_TIME WARNER INC.</th>                      <td>    0.0049</td> <td>    0.002</td> <td>    2.324</td> <td> 0.020</td> <td>    0.001     0.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_TWITTER INC</th>                           <td>    0.0185</td> <td>    0.006</td> <td>    3.201</td> <td> 0.001</td> <td>    0.007     0.030</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_UNION PACIFIC CORPORATION</th>             <td>    0.0130</td> <td>    0.004</td> <td>    3.275</td> <td> 0.001</td> <td>    0.005     0.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_UNITED CONTINENTAL HOLDINGS IN</th>        <td>    0.0097</td> <td>    0.005</td> <td>    2.019</td> <td> 0.044</td> <td>    0.000     0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_UNITED PARCEL SERVICE INC.CL B</th>        <td>    0.0101</td> <td>    0.005</td> <td>    2.184</td> <td> 0.029</td> <td>    0.001     0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_UNITED TECHNOLOGIES CORP</th>              <td>    0.0113</td> <td>    0.004</td> <td>    2.830</td> <td> 0.005</td> <td>    0.003     0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_UNITEDHEALTH GROUP INC</th>                <td>    0.0100</td> <td>    0.004</td> <td>    2.453</td> <td> 0.014</td> <td>    0.002     0.018</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_VALERO ENERGY CORP (NEW)</th>              <td>    0.0099</td> <td>    0.004</td> <td>    2.405</td> <td> 0.016</td> <td>    0.002     0.018</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_VERIZON COMMUNICATIONS</th>                <td>    0.0116</td> <td>    0.005</td> <td>    2.369</td> <td> 0.018</td> <td>    0.002     0.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_VISA INC</th>                              <td>    0.0189</td> <td>    0.005</td> <td>    3.588</td> <td> 0.000</td> <td>    0.009     0.029</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_WALGREENS BOOTS ALLIANCE INC</th>          <td>    0.0109</td> <td>    0.004</td> <td>    2.732</td> <td> 0.006</td> <td>    0.003     0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_WALMART INC</th>                           <td>    0.0078</td> <td>    0.006</td> <td>    1.208</td> <td> 0.227</td> <td>   -0.005     0.020</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_WALT DISNEY CO</th>                        <td>    0.0102</td> <td>    0.003</td> <td>    3.309</td> <td> 0.001</td> <td>    0.004     0.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_WELLS FARGO & CO(NEW)</th>                 <td>    0.0434</td> <td>    0.013</td> <td>    3.348</td> <td> 0.001</td> <td>    0.018     0.069</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_WILLIAMS COMPANIES</th>                    <td>    0.0109</td> <td>    0.004</td> <td>    2.720</td> <td> 0.007</td> <td>    0.003     0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_WYNN RESORTS LTD</th>                      <td>    0.0109</td> <td>    0.005</td> <td>    2.254</td> <td> 0.024</td> <td>    0.001     0.020</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_YELP INC</th>                              <td>    0.0231</td> <td>    0.006</td> <td>    3.978</td> <td> 0.000</td> <td>    0.012     0.034</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>41.371</td> <th>  Durbin-Watson:     </th> <td>   1.329</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  45.849</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.034</td> <th>  Prob(JB):          </th> <td>1.11e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.141</td> <th>  Cond. No.          </th> <td>5.40e+21</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:              Returns1D   R-squared:                       0.004\n",
       "Model:                            OLS   Adj. R-squared:                  0.000\n",
       "Method:                 Least Squares   F-statistic:                     1.084\n",
       "Date:                Sun, 09 Sep 2018   Prob (F-statistic):              0.214\n",
       "Time:                        21:17:58   Log-Likelihood:             1.3306e+05\n",
       "No. Observations:               44878   AIC:                        -2.658e+05\n",
       "Df Residuals:                   44703   BIC:                        -2.642e+05\n",
       "Df Model:                         174                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===============================================================================================================\n",
       "                                                  coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
       "---------------------------------------------------------------------------------------------------------------\n",
       "DividendYield                                1.249e-06    4.7e-07      2.659      0.008      3.28e-07  2.17e-06\n",
       "EBITDAYield                                 -5.537e-06   9.48e-06     -0.584      0.559     -2.41e-05   1.3e-05\n",
       "EVToEBITDA                                    4.87e-06    8.7e-06      0.560      0.575     -1.22e-05  2.19e-05\n",
       "EVToFCF                                      3.621e-06   1.08e-05      0.334      0.738     -1.76e-05  2.48e-05\n",
       "PriceToBook                                 -5.411e-07   1.03e-05     -0.052      0.958     -2.08e-05  1.97e-05\n",
       "PriceToDilutedEarningsTTM                    4.561e-06   4.64e-06      0.983      0.326     -4.54e-06  1.37e-05\n",
       "PriceToEarningsTTM                               6e-08   2.05e-07      0.293      0.770     -3.41e-07  4.61e-07\n",
       "PriceToFCF                                  -9.612e-07   9.07e-06     -0.106      0.916     -1.87e-05  1.68e-05\n",
       "PriceToOperatingCashflow                    -6.258e-07   6.16e-06     -0.102      0.919     -1.27e-05  1.14e-05\n",
       "PriceToSalesTTM                             -2.088e-06   5.16e-07     -4.048      0.000      -3.1e-06 -1.08e-06\n",
       "Directional Movement Index                  -3.169e-07   2.09e-06     -0.152      0.879      -4.4e-06  3.77e-06\n",
       "Money Flow Index                            -2.768e-06    2.7e-06     -1.025      0.305     -8.06e-06  2.52e-06\n",
       "Percent Above Low                            2.875e-06   5.17e-06      0.556      0.578     -7.26e-06   1.3e-05\n",
       "Percent Below High                          -1.786e-06   4.14e-06     -0.431      0.666      -9.9e-06  6.33e-06\n",
       "Price Oscillator                              9.18e-07   2.92e-06      0.314      0.753     -4.81e-06  6.64e-06\n",
       "Trendline                                    2.007e-06   4.41e-06      0.455      0.649     -6.64e-06  1.07e-05\n",
       "AssetToEquityRatio                           5.633e-07   3.98e-07      1.414      0.157     -2.17e-07  1.34e-06\n",
       "AssetTurnover                                -1.27e-05   1.74e-05     -0.731      0.465     -4.68e-05  2.14e-05\n",
       "CurrentRatio                                 3.246e-07    5.2e-07      0.625      0.532     -6.94e-07  1.34e-06\n",
       "DebtToAssetRatio                             5.697e-07   3.18e-07      1.792      0.073     -5.35e-08  1.19e-06\n",
       "DebtToEquityRatio                           -5.451e-07   3.69e-07     -1.476      0.140     -1.27e-06  1.79e-07\n",
       "MertonsDD                                      -0.0001   4.64e-05     -2.661      0.008        -0.000 -3.25e-05\n",
       "WorkingCapitalToAssets                       1.452e-07   6.31e-07      0.230      0.818     -1.09e-06  1.38e-06\n",
       "WorkingCapitalToSales                        -2.24e-05   2.03e-05     -1.106      0.269     -6.21e-05  1.73e-05\n",
       "Dividend Growth                             -1.505e-08   1.96e-07     -0.077      0.939        -4e-07  3.69e-07\n",
       "EPS                                         -1.219e-07    3.1e-07     -0.393      0.694      -7.3e-07  4.86e-07\n",
       "Net Debt                                    -1.968e-14   1.12e-14     -1.760      0.078     -4.16e-14  2.23e-15\n",
       "Sales                                         5.85e-15   1.04e-14      0.563      0.574     -1.45e-14  2.62e-14\n",
       "Total Assets                                -1.665e-14   7.87e-15     -2.116      0.034     -3.21e-14 -1.23e-15\n",
       "EPS Growth 3M                               -5.625e-08   4.75e-07     -0.118      0.906     -9.87e-07  8.75e-07\n",
       "EPS Growth 12M                               6.686e-07   4.76e-07      1.405      0.160     -2.64e-07   1.6e-06\n",
       "Net Debt Growth 3M                           8.433e-08   6.03e-07      0.140      0.889      -1.1e-06  1.27e-06\n",
       "Net Debt Growth 12M                          4.558e-07   6.14e-07      0.743      0.458     -7.47e-07  1.66e-06\n",
       "Sales Growth 3M                              2.469e-07    6.4e-07      0.386      0.699     -1.01e-06   1.5e-06\n",
       "Sales Growth 12M                            -4.861e-07   6.53e-07     -0.745      0.456     -1.77e-06  7.93e-07\n",
       "Total Assets Growth 3M                       4.535e-07   7.16e-07      0.633      0.527      -9.5e-07  1.86e-06\n",
       "Total Assets Growth 12M                     -3.477e-07   7.64e-07     -0.455      0.649     -1.84e-06  1.15e-06\n",
       "CFO To Assets                                1.731e-05   8.66e-06      1.999      0.046      3.36e-07  3.43e-05\n",
       "Capex To Assets                             -2.097e-05   1.87e-05     -1.124      0.261     -5.75e-05  1.56e-05\n",
       "Capex To FCF                                  1.29e-06   1.05e-05      0.123      0.902     -1.92e-05  2.18e-05\n",
       "Capex To Sales                               1.637e-05      2e-05      0.820      0.412     -2.28e-05  5.55e-05\n",
       "EBIT To Assets                               7.612e-06   8.75e-06      0.870      0.385     -9.55e-06  2.48e-05\n",
       "Retained Earnings To Assets                 -3.547e-05   1.84e-05     -1.923      0.054     -7.16e-05  6.76e-07\n",
       "Downside Risk                                4.943e-07   5.32e-06      0.093      0.926     -9.93e-06  1.09e-05\n",
       "Index Beta                                  -8.585e-08   1.09e-07     -0.786      0.432        -3e-07  1.28e-07\n",
       "Log Market Cap                               1.847e-05   1.86e-05      0.995      0.320     -1.79e-05  5.49e-05\n",
       "Volatility 3M                               -8.138e-06   4.53e-06     -1.798      0.072      -1.7e-05  7.33e-07\n",
       "stock_3D SYSTEMS CORP                           0.0157      0.004      3.600      0.000         0.007     0.024\n",
       "stock_3M COMPANY                                0.0108      0.004      2.823      0.005         0.003     0.018\n",
       "stock_ABBOTT LABORATORIES                       0.0069      0.003      2.315      0.021         0.001     0.013\n",
       "stock_ABBVIE INC                                0.0163      0.006      2.916      0.004         0.005     0.027\n",
       "stock_ALLERGAN INC                              0.0103      0.003      3.563      0.000         0.005     0.016\n",
       "stock_ALLERGAN PLC                              0.0135      0.004      3.310      0.001         0.006     0.022\n",
       "stock_ALTABA INC                                0.0161      0.005      3.560      0.000         0.007     0.025\n",
       "stock_ALTRIA GROUP INC.                         0.0111      0.004      2.847      0.004         0.003     0.019\n",
       "stock_AMAZON.COM INC                            0.0123      0.005      2.683      0.007         0.003     0.021\n",
       "stock_AMERICAN AIRLINES GROUP INC               0.0143      0.005      2.720      0.007         0.004     0.025\n",
       "stock_AMERICAN EXPRESS COMPANY                  0.0065      0.002      2.656      0.008         0.002     0.011\n",
       "stock_AMERICAN INTL GROUP INC                   0.0118      0.004      2.850      0.004         0.004     0.020\n",
       "stock_AMGEN INC                                 0.0064      0.003      2.421      0.015         0.001     0.012\n",
       "stock_ANADARKO PETROLEUM CORP                   0.0077      0.002      3.231      0.001         0.003     0.012\n",
       "stock_APACHE CORP                               0.0045      0.004      1.262      0.207        -0.003     0.012\n",
       "stock_APPLE INC                                 0.0086      0.004      2.278      0.023         0.001     0.016\n",
       "stock_APPLIED MATERIALS INC                     0.0075      0.003      2.572      0.010         0.002     0.013\n",
       "stock_ARCONIC INC                               0.0026      0.002      1.210      0.226        -0.002     0.007\n",
       "stock_AT&T INC. COM                             0.0103      0.004      2.420      0.016         0.002     0.019\n",
       "stock_Alphabet Inc. Cl A                        0.0192      0.005      3.528      0.000         0.009     0.030\n",
       "stock_BAKER HUGHES INC                          0.0078      0.003      2.946      0.003         0.003     0.013\n",
       "stock_BANK OF AMERICA CORP                      0.0404      0.016      2.532      0.011         0.009     0.072\n",
       "stock_BERKSHIRE HATHAWAY INC CL-B               0.0171      0.006      3.069      0.002         0.006     0.028\n",
       "stock_BIOGEN INC                                0.0120      0.004      3.275      0.001         0.005     0.019\n",
       "stock_BOEING CO                                 0.0053      0.003      1.864      0.062        -0.000     0.011\n",
       "stock_BOOKING HOLDINGS INC                      0.0153      0.005      3.232      0.001         0.006     0.025\n",
       "stock_BRISTOL MYERS SQUIBB COMPANY              0.0097      0.003      3.001      0.003         0.003     0.016\n",
       "stock_BROADCOM CORP                             0.0134      0.004      3.087      0.002         0.005     0.022\n",
       "stock_BROADCOM INC                              0.0173      0.006      3.112      0.002         0.006     0.028\n",
       "stock_CATERPILLAR INC                           0.0042      0.003      1.502      0.133        -0.001     0.010\n",
       "stock_CELGENE CORP                              0.0091      0.003      2.905      0.004         0.003     0.015\n",
       "stock_CHESAPEAKE ENERGY CORP                    0.0041      0.005      0.874      0.382        -0.005     0.013\n",
       "stock_CHEVRON CORPORATION                       0.0137      0.006      2.386      0.017         0.002     0.025\n",
       "stock_CISCO SYSTEMS INC                         0.0066      0.003      2.363      0.018         0.001     0.012\n",
       "stock_CITIGROUP                                 0.0371      0.014      2.713      0.007         0.010     0.064\n",
       "stock_COCA-COLA CO                              0.0101      0.004      2.629      0.009         0.003     0.018\n",
       "stock_COMCAST CORP                              0.0081      0.003      2.917      0.004         0.003     0.013\n",
       "stock_CONOCOPHILLIPS                            0.0125      0.005      2.566      0.010         0.003     0.022\n",
       "stock_COVIDIEN PLC                              0.0196      0.005      3.839      0.000         0.010     0.030\n",
       "stock_CVS HEALTH CORP                           0.0077      0.004      2.122      0.034         0.001     0.015\n",
       "stock_DEERE & CO                                0.0050      0.003      1.671      0.095        -0.001     0.011\n",
       "stock_DELTA AIR LINES INC                       0.0141      0.005      2.882      0.004         0.005     0.024\n",
       "stock_DIRECTV                                   0.0114      0.005      2.405      0.016         0.002     0.021\n",
       "stock_DOLLAR GENERAL CORP                       0.0143      0.005      2.899      0.004         0.005     0.024\n",
       "stock_DOW CHEMICAL CO                           0.0055      0.003      1.960      0.050     -5.93e-07     0.011\n",
       "stock_E.I. Du Pont De Nemours A                 0.0060      0.003      2.109      0.035         0.000     0.012\n",
       "stock_EBAY INC                                  0.0166      0.005      3.281      0.001         0.007     0.027\n",
       "stock_EMC CORPORATION                           0.0082      0.003      2.886      0.004         0.003     0.014\n",
       "stock_EOG RESOURCES INC                         0.0099      0.003      3.577      0.000         0.004     0.015\n",
       "stock_EXPRESS SCRIPTS HOLDING CO                0.0034      0.003      1.166      0.244        -0.002     0.009\n",
       "stock_EXXON MOBIL CORPORATION                   0.0128      0.007      1.936      0.053        -0.000     0.026\n",
       "stock_FACEBOOK INC                              0.0199      0.006      3.452      0.001         0.009     0.031\n",
       "stock_FEDEX CORPORATION                         0.0079      0.003      2.618      0.009         0.002     0.014\n",
       "stock_FIRST SOLAR INC                           0.0180      0.005      3.411      0.001         0.008     0.028\n",
       "stock_FORD MOTOR CO(NEW)                        0.0057      0.004      1.626      0.104        -0.001     0.013\n",
       "stock_FREEPORT-MCMORAN INC                      0.0090      0.004      2.333      0.020         0.001     0.017\n",
       "stock_GENERAL ELECTRIC CO                       0.0165      0.005      3.025      0.002         0.006     0.027\n",
       "stock_GENERAL MOTORS CO                         0.0131      0.006      2.358      0.018         0.002     0.024\n",
       "stock_GILEAD SCIENCES INC                       0.0108      0.003      3.141      0.002         0.004     0.017\n",
       "stock_GOLDMAN SACHS GROUP INC                   0.0317      0.007      4.257      0.000         0.017     0.046\n",
       "stock_GOPRO INC                                 0.0199      0.006      3.550      0.000         0.009     0.031\n",
       "stock_HALLIBURTON CO (HOLDING CO)               0.0084      0.003      2.752      0.006         0.002     0.014\n",
       "stock_HOME DEPOT INC                            0.0070      0.003      2.052      0.040         0.000     0.014\n",
       "stock_HP INC                                    0.0051      0.003      1.624      0.104        -0.001     0.011\n",
       "stock_INTEL CORP                                0.0087      0.003      2.535      0.011         0.002     0.015\n",
       "stock_INTL BUSINESS MACHINES CORP               0.0087      0.004      2.280      0.023         0.001     0.016\n",
       "stock_JOHNSON AND JOHNSON                       0.0111      0.004      2.834      0.005         0.003     0.019\n",
       "stock_JPMORGAN CHASE & CO COM STK               0.0545      0.019      2.818      0.005         0.017     0.092\n",
       "stock_KEURIG GREEN MOUNTAIN INC                 0.0146      0.004      3.591      0.000         0.007     0.023\n",
       "stock_KINDER MORGAN INC                         0.0132      0.005      2.470      0.014         0.003     0.024\n",
       "stock_LAS VEGAS SANDS CORP                      0.0127      0.005      2.488      0.013         0.003     0.023\n",
       "stock_LILLY ELI & CO                            0.0102      0.004      2.885      0.004         0.003     0.017\n",
       "stock_LINKEDIN CORP                             0.0197      0.006      3.505      0.000         0.009     0.031\n",
       "stock_LOWES COMPANIES INC                       0.0075      0.003      2.483      0.013         0.002     0.013\n",
       "stock_LYONDELLBASELL INDUSTRIES NV              0.0135      0.005      2.593      0.010         0.003     0.024\n",
       "stock_MARATHON PETROLEUM CORP                   0.0128      0.006      2.268      0.023         0.002     0.024\n",
       "stock_MASTERCARD INCORPORATED                   0.0206      0.006      3.731      0.000         0.010     0.031\n",
       "stock_MCDONALDS CORP                            0.0099      0.004      2.513      0.012         0.002     0.018\n",
       "stock_MEDTRONIC PLC                             0.0119      0.004      3.390      0.001         0.005     0.019\n",
       "stock_MERCK & CO INC                            0.0110      0.004      2.966      0.003         0.004     0.018\n",
       "stock_METLIFE  INC                              0.0260      0.008      3.353      0.001         0.011     0.041\n",
       "stock_MICHAEL KORS HOLDINGS LTD                 0.0214      0.006      3.646      0.000         0.010     0.033\n",
       "stock_MICRON TECHNOLOGY INC                     0.0102      0.003      3.287      0.001         0.004     0.016\n",
       "stock_MICROSOFT CORP                            0.0115      0.004      3.028      0.002         0.004     0.019\n",
       "stock_MONDELEZ INTERNATIONAL INC                0.0133      0.005      2.875      0.004         0.004     0.022\n",
       "stock_MONSANTO COMPANY                          0.0153      0.005      3.201      0.001         0.006     0.025\n",
       "stock_MORGAN STANLEY                            0.0273      0.007      3.884      0.000         0.014     0.041\n",
       "stock_MYLAN NV                                  0.0111      0.003      3.260      0.001         0.004     0.018\n",
       "stock_NATIONAL OILWELL VARCO  INC.              0.0131      0.005      2.642      0.008         0.003     0.023\n",
       "stock_NETFLIX INC                               0.0179      0.005      3.555      0.000         0.008     0.028\n",
       "stock_NEWMONT MINING CORP (HOLDING COMPANY)     0.0098      0.004      2.773      0.006         0.003     0.017\n",
       "stock_NEWS CP - CL A                            0.0129      0.004      3.348      0.001         0.005     0.020\n",
       "stock_NIKE INC CL-B                             0.0104      0.004      2.884      0.004         0.003     0.018\n",
       "stock_OCCIDENTAL PETROLEUM CORP                 0.0118      0.004      3.153      0.002         0.004     0.019\n",
       "stock_ORACLE CORP                               0.0118      0.004      3.297      0.001         0.005     0.019\n",
       "stock_PANDORA MEDIA INC                         0.0235      0.006      3.942      0.000         0.012     0.035\n",
       "stock_PENNEY J.C. CO INC (HOLDING COMPANY)      0.0027      0.003      0.905      0.365        -0.003     0.009\n",
       "stock_PEPSICO INC                               0.0104      0.004      2.533      0.011         0.002     0.018\n",
       "stock_PFIZER INC                                0.0134      0.004      3.287      0.001         0.005     0.021\n",
       "stock_PHILIP MORRIS INTERNATIONAL INC           0.0154      0.006      2.576      0.010         0.004     0.027\n",
       "stock_PIONEER NAT RES CO                        0.0177      0.004      4.022      0.000         0.009     0.026\n",
       "stock_PRECISION CASTPARTS CORP                  0.0145      0.004      3.574      0.000         0.007     0.022\n",
       "stock_PROCTER & GAMBLE CO                       0.0115      0.004      2.722      0.006         0.003     0.020\n",
       "stock_QUALCOMM INC                              0.0131      0.004      3.223      0.001         0.005     0.021\n",
       "stock_REGENERON PHARMACEUTICALS INC             0.0117      0.005      2.380      0.017         0.002     0.021\n",
       "stock_SALESFORCE.COM INC                        0.0173      0.005      3.443      0.001         0.007     0.027\n",
       "stock_SALIX PHARMACEUTICALS LTD             -2.111e-18   7.34e-17     -0.029      0.977     -1.46e-16  1.42e-16\n",
       "stock_SANDISK CORP                              0.0154      0.004      3.629      0.000         0.007     0.024\n",
       "stock_SCHLUMBERGER LTD.                         0.0120      0.004      3.003      0.003         0.004     0.020\n",
       "stock_SKYWORKS SOLUTIONS INC                    0.0191      0.005      3.796      0.000         0.009     0.029\n",
       "stock_SOLARCITY CORP                            0.0208      0.006      3.549      0.000         0.009     0.032\n",
       "stock_SOUTHWEST AIRLINES CO                     0.0092      0.003      2.885      0.004         0.003     0.015\n",
       "stock_STARBUCKS CORPORATION                     0.0147      0.004      3.658      0.000         0.007     0.023\n",
       "stock_SUNEDISON INC                             0.0183      0.006      3.237      0.001         0.007     0.029\n",
       "stock_TARGET CORPORATION                        0.0104      0.004      2.349      0.019         0.002     0.019\n",
       "stock_TESLA INC                                 0.0196      0.006      3.520      0.000         0.009     0.031\n",
       "stock_TEXAS INSTRUMENTS INC                     0.0141      0.004      3.229      0.001         0.006     0.023\n",
       "stock_TIME WARNER CABLE INC                     0.0140      0.005      2.883      0.004         0.004     0.024\n",
       "stock_TIME WARNER INC.                          0.0049      0.002      2.324      0.020         0.001     0.009\n",
       "stock_TWITTER INC                               0.0185      0.006      3.201      0.001         0.007     0.030\n",
       "stock_UNION PACIFIC CORPORATION                 0.0130      0.004      3.275      0.001         0.005     0.021\n",
       "stock_UNITED CONTINENTAL HOLDINGS IN            0.0097      0.005      2.019      0.044         0.000     0.019\n",
       "stock_UNITED PARCEL SERVICE INC.CL B            0.0101      0.005      2.184      0.029         0.001     0.019\n",
       "stock_UNITED TECHNOLOGIES CORP                  0.0113      0.004      2.830      0.005         0.003     0.019\n",
       "stock_UNITEDHEALTH GROUP INC                    0.0100      0.004      2.453      0.014         0.002     0.018\n",
       "stock_VALERO ENERGY CORP (NEW)                  0.0099      0.004      2.405      0.016         0.002     0.018\n",
       "stock_VERIZON COMMUNICATIONS                    0.0116      0.005      2.369      0.018         0.002     0.021\n",
       "stock_VISA INC                                  0.0189      0.005      3.588      0.000         0.009     0.029\n",
       "stock_WALGREENS BOOTS ALLIANCE INC              0.0109      0.004      2.732      0.006         0.003     0.019\n",
       "stock_WALMART INC                               0.0078      0.006      1.208      0.227        -0.005     0.020\n",
       "stock_WALT DISNEY CO                            0.0102      0.003      3.309      0.001         0.004     0.016\n",
       "stock_WELLS FARGO & CO(NEW)                     0.0434      0.013      3.348      0.001         0.018     0.069\n",
       "stock_WILLIAMS COMPANIES                        0.0109      0.004      2.720      0.007         0.003     0.019\n",
       "stock_WYNN RESORTS LTD                          0.0109      0.005      2.254      0.024         0.001     0.020\n",
       "stock_YELP INC                                  0.0231      0.006      3.978      0.000         0.012     0.034\n",
       "==============================================================================\n",
       "Omnibus:                       41.371   Durbin-Watson:                   1.329\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               45.849\n",
       "Skew:                          -0.034   Prob(JB):                     1.11e-10\n",
       "Kurtosis:                       3.141   Cond. No.                     5.40e+21\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The smallest eigenvalue is 1.4e-16. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target = 'Returns1D'\n",
    "model_data = pd.concat([y[[target]], X], axis=1).dropna()\n",
    "model_data = model_data[model_data[target].between(model_data[target].quantile(.025), \n",
    "                                                   model_data[target].quantile(.975))]\n",
    "\n",
    "model = OLS(endog=model_data[target], exog=model_data.drop(target, axis=1))\n",
    "trained_model = model.fit()\n",
    "trained_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The summary is available in the notebook to save some space due to the large number of variables. The diagnostic statistics show that, given the high p-value on the Jarque—Bera statistic, the hypothesis that the residuals are normally distributed cannot be rejected.\n",
    "\n",
    "However, the Durbin—Watson statistic is low at 1.5 so we can reject the null hypothesis of no autocorrelation comfortably at the 5% level. Hence, the standard errors are likely positively correlated. If our goal were to understand which factors are significantly associated with forward returns, we would need to rerun the regression using robust standard errors (a parameter in statsmodels .fit() method), or use a different method altogether such as a panel model that allows for more complex error covariance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>Returns5D</td>    <th>  R-squared:         </th>  <td>   0.016</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.012</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   4.124</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 09 Sep 2018</td> <th>  Prob (F-statistic):</th>  <td>2.28e-66</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:18:00</td>     <th>  Log-Likelihood:    </th>  <td>  95279.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 44370</td>      <th>  AIC:               </th> <td>-1.902e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 44195</td>      <th>  BIC:               </th> <td>-1.887e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>   174</td>      <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                       <td></td>                          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>DividendYield</th>                               <td> 4.504e-06</td> <td> 1.08e-06</td> <td>    4.185</td> <td> 0.000</td> <td> 2.39e-06  6.61e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>EBITDAYield</th>                                 <td> 9.323e-06</td> <td> 2.17e-05</td> <td>    0.430</td> <td> 0.667</td> <td>-3.32e-05  5.19e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>EVToEBITDA</th>                                  <td> 1.425e-05</td> <td> 1.98e-05</td> <td>    0.719</td> <td> 0.472</td> <td>-2.46e-05  5.31e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>EVToFCF</th>                                     <td> 2.654e-05</td> <td> 2.47e-05</td> <td>    1.075</td> <td> 0.283</td> <td>-2.19e-05  7.49e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PriceToBook</th>                                 <td> 1.582e-05</td> <td> 2.36e-05</td> <td>    0.669</td> <td> 0.503</td> <td>-3.05e-05  6.21e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PriceToDilutedEarningsTTM</th>                   <td> 5.462e-07</td> <td> 1.06e-05</td> <td>    0.052</td> <td> 0.959</td> <td>-2.01e-05  2.12e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PriceToEarningsTTM</th>                          <td> 1.449e-07</td> <td> 4.67e-07</td> <td>    0.310</td> <td> 0.757</td> <td>-7.71e-07  1.06e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PriceToFCF</th>                                  <td> -1.94e-05</td> <td> 2.07e-05</td> <td>   -0.939</td> <td> 0.348</td> <td>-5.99e-05  2.11e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PriceToOperatingCashflow</th>                    <td>-4.155e-05</td> <td> 1.42e-05</td> <td>   -2.934</td> <td> 0.003</td> <td>-6.93e-05 -1.38e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PriceToSalesTTM</th>                             <td>-8.975e-06</td> <td> 1.19e-06</td> <td>   -7.530</td> <td> 0.000</td> <td>-1.13e-05 -6.64e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Directional Movement Index</th>                  <td>-4.457e-06</td> <td> 4.75e-06</td> <td>   -0.938</td> <td> 0.348</td> <td>-1.38e-05  4.86e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Money Flow Index</th>                            <td>-5.894e-06</td> <td> 6.16e-06</td> <td>   -0.956</td> <td> 0.339</td> <td> -1.8e-05  6.19e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Percent Above Low</th>                           <td>-1.985e-05</td> <td> 1.18e-05</td> <td>   -1.675</td> <td> 0.094</td> <td>-4.31e-05  3.38e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Percent Below High</th>                          <td> 7.651e-06</td> <td> 9.42e-06</td> <td>    0.812</td> <td> 0.417</td> <td>-1.08e-05  2.61e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Price Oscillator</th>                            <td> 3.706e-06</td> <td> 6.64e-06</td> <td>    0.558</td> <td> 0.577</td> <td>-9.31e-06  1.67e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Trendline</th>                                   <td> 1.414e-05</td> <td> 1.01e-05</td> <td>    1.402</td> <td> 0.161</td> <td>-5.64e-06  3.39e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AssetToEquityRatio</th>                          <td>-1.226e-07</td> <td> 8.91e-07</td> <td>   -0.138</td> <td> 0.891</td> <td>-1.87e-06  1.62e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AssetTurnover</th>                               <td>  -7.3e-06</td> <td> 3.95e-05</td> <td>   -0.185</td> <td> 0.854</td> <td>-8.48e-05  7.02e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CurrentRatio</th>                                <td>  4.11e-07</td> <td> 1.18e-06</td> <td>    0.347</td> <td> 0.728</td> <td>-1.91e-06  2.73e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>DebtToAssetRatio</th>                            <td> 2.484e-06</td> <td> 7.25e-07</td> <td>    3.427</td> <td> 0.001</td> <td> 1.06e-06   3.9e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>DebtToEquityRatio</th>                           <td>-5.365e-07</td> <td> 8.37e-07</td> <td>   -0.641</td> <td> 0.521</td> <td>-2.18e-06   1.1e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MertonsDD</th>                                   <td>   -0.0004</td> <td>    0.000</td> <td>   -3.508</td> <td> 0.000</td> <td>   -0.001    -0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WorkingCapitalToAssets</th>                      <td> 1.341e-06</td> <td> 1.44e-06</td> <td>    0.932</td> <td> 0.351</td> <td>-1.48e-06  4.16e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WorkingCapitalToSales</th>                       <td>-3.118e-05</td> <td> 4.62e-05</td> <td>   -0.674</td> <td> 0.500</td> <td>   -0.000  5.95e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dividend Growth</th>                             <td>-2.629e-07</td> <td>  4.5e-07</td> <td>   -0.585</td> <td> 0.559</td> <td>-1.14e-06  6.18e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>EPS</th>                                         <td> 2.901e-07</td> <td> 7.04e-07</td> <td>    0.412</td> <td> 0.680</td> <td>-1.09e-06  1.67e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Net Debt</th>                                    <td>-8.116e-14</td> <td> 2.56e-14</td> <td>   -3.172</td> <td> 0.002</td> <td>-1.31e-13  -3.1e-14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sales</th>                                       <td>-5.373e-14</td> <td>  2.4e-14</td> <td>   -2.239</td> <td> 0.025</td> <td>-1.01e-13 -6.69e-15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Total Assets</th>                                <td>-4.104e-14</td> <td>  1.8e-14</td> <td>   -2.276</td> <td> 0.023</td> <td>-7.64e-14  -5.7e-15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>EPS Growth 3M</th>                               <td>-2.787e-06</td> <td> 1.08e-06</td> <td>   -2.582</td> <td> 0.010</td> <td> -4.9e-06 -6.72e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>EPS Growth 12M</th>                              <td> 1.257e-06</td> <td> 1.08e-06</td> <td>    1.161</td> <td> 0.246</td> <td>-8.64e-07  3.38e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Net Debt Growth 3M</th>                          <td> 2.628e-07</td> <td> 1.37e-06</td> <td>    0.191</td> <td> 0.848</td> <td>-2.43e-06  2.95e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Net Debt Growth 12M</th>                         <td> 2.592e-06</td> <td>  1.4e-06</td> <td>    1.858</td> <td> 0.063</td> <td>-1.43e-07  5.33e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sales Growth 3M</th>                             <td> 3.824e-06</td> <td> 1.45e-06</td> <td>    2.634</td> <td> 0.008</td> <td> 9.79e-07  6.67e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sales Growth 12M</th>                            <td> -2.38e-06</td> <td> 1.48e-06</td> <td>   -1.604</td> <td> 0.109</td> <td>-5.29e-06  5.28e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Total Assets Growth 3M</th>                      <td>-6.292e-07</td> <td> 1.63e-06</td> <td>   -0.386</td> <td> 0.699</td> <td>-3.82e-06  2.56e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Total Assets Growth 12M</th>                     <td> -1.46e-06</td> <td> 1.73e-06</td> <td>   -0.842</td> <td> 0.400</td> <td>-4.86e-06  1.94e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CFO To Assets</th>                               <td> 4.634e-05</td> <td> 1.98e-05</td> <td>    2.342</td> <td> 0.019</td> <td> 7.56e-06  8.51e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Capex To Assets</th>                             <td>   -0.0001</td> <td> 4.26e-05</td> <td>   -3.232</td> <td> 0.001</td> <td>   -0.000 -5.42e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Capex To FCF</th>                                <td> 3.673e-05</td> <td> 2.39e-05</td> <td>    1.534</td> <td> 0.125</td> <td>-1.02e-05  8.37e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Capex To Sales</th>                              <td>    0.0001</td> <td> 4.56e-05</td> <td>    2.247</td> <td> 0.025</td> <td> 1.31e-05     0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>EBIT To Assets</th>                              <td> 8.146e-06</td> <td>    2e-05</td> <td>    0.408</td> <td> 0.683</td> <td> -3.1e-05  4.73e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Retained Earnings To Assets</th>                 <td>   -0.0001</td> <td>  4.2e-05</td> <td>   -3.036</td> <td> 0.002</td> <td>   -0.000 -4.51e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Downside Risk</th>                               <td> 1.166e-05</td> <td> 1.22e-05</td> <td>    0.960</td> <td> 0.337</td> <td>-1.22e-05  3.55e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Index Beta</th>                                  <td>-7.363e-07</td> <td> 2.49e-07</td> <td>   -2.958</td> <td> 0.003</td> <td>-1.22e-06 -2.48e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Log Market Cap</th>                              <td> 9.103e-05</td> <td> 4.24e-05</td> <td>    2.148</td> <td> 0.032</td> <td> 7.98e-06     0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Volatility 3M</th>                               <td>-5.492e-05</td> <td> 1.04e-05</td> <td>   -5.306</td> <td> 0.000</td> <td>-7.52e-05 -3.46e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_3D SYSTEMS CORP</th>                       <td>    0.0568</td> <td>    0.010</td> <td>    5.697</td> <td> 0.000</td> <td>    0.037     0.076</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_3M COMPANY</th>                            <td>    0.0426</td> <td>    0.009</td> <td>    4.865</td> <td> 0.000</td> <td>    0.025     0.060</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_ABBOTT LABORATORIES</th>                   <td>    0.0264</td> <td>    0.007</td> <td>    3.854</td> <td> 0.000</td> <td>    0.013     0.040</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_ABBVIE INC</th>                            <td>    0.0572</td> <td>    0.013</td> <td>    4.482</td> <td> 0.000</td> <td>    0.032     0.082</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_ALLERGAN INC</th>                          <td>    0.0487</td> <td>    0.007</td> <td>    7.349</td> <td> 0.000</td> <td>    0.036     0.062</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_ALLERGAN PLC</th>                          <td>    0.0543</td> <td>    0.009</td> <td>    5.812</td> <td> 0.000</td> <td>    0.036     0.073</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_ALTABA INC</th>                            <td>    0.0605</td> <td>    0.010</td> <td>    5.857</td> <td> 0.000</td> <td>    0.040     0.081</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_ALTRIA GROUP INC.</th>                     <td>    0.0453</td> <td>    0.009</td> <td>    5.074</td> <td> 0.000</td> <td>    0.028     0.063</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_AMAZON.COM INC</th>                        <td>    0.0513</td> <td>    0.010</td> <td>    4.905</td> <td> 0.000</td> <td>    0.031     0.072</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_AMERICAN AIRLINES GROUP INC</th>           <td>    0.0490</td> <td>    0.012</td> <td>    4.079</td> <td> 0.000</td> <td>    0.025     0.073</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_AMERICAN EXPRESS COMPANY</th>              <td>    0.0232</td> <td>    0.006</td> <td>    4.177</td> <td> 0.000</td> <td>    0.012     0.034</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_AMERICAN INTL GROUP INC</th>               <td>    0.0452</td> <td>    0.009</td> <td>    4.770</td> <td> 0.000</td> <td>    0.027     0.064</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_AMGEN INC</th>                             <td>    0.0266</td> <td>    0.006</td> <td>    4.400</td> <td> 0.000</td> <td>    0.015     0.038</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_ANADARKO PETROLEUM CORP</th>               <td>    0.0347</td> <td>    0.005</td> <td>    6.387</td> <td> 0.000</td> <td>    0.024     0.045</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_APACHE CORP</th>                           <td>    0.0130</td> <td>    0.009</td> <td>    1.487</td> <td> 0.137</td> <td>   -0.004     0.030</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_APPLE INC</th>                             <td>    0.0471</td> <td>    0.009</td> <td>    5.478</td> <td> 0.000</td> <td>    0.030     0.064</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_APPLIED MATERIALS INC</th>                 <td>    0.0333</td> <td>    0.007</td> <td>    4.965</td> <td> 0.000</td> <td>    0.020     0.046</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_ARCONIC INC</th>                           <td>    0.0090</td> <td>    0.005</td> <td>    1.822</td> <td> 0.068</td> <td>   -0.001     0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_AT&T INC. COM</th>                         <td>    0.0372</td> <td>    0.010</td> <td>    3.817</td> <td> 0.000</td> <td>    0.018     0.056</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_Alphabet Inc. Cl A</th>                    <td>    0.0713</td> <td>    0.012</td> <td>    5.733</td> <td> 0.000</td> <td>    0.047     0.096</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_BAKER HUGHES INC</th>                      <td>    0.0296</td> <td>    0.006</td> <td>    4.920</td> <td> 0.000</td> <td>    0.018     0.041</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_BANK OF AMERICA CORP</th>                  <td>    0.1209</td> <td>    0.037</td> <td>    3.306</td> <td> 0.001</td> <td>    0.049     0.193</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_BERKSHIRE HATHAWAY INC CL-B</th>           <td>    0.0629</td> <td>    0.013</td> <td>    4.939</td> <td> 0.000</td> <td>    0.038     0.088</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_BIOGEN INC</th>                            <td>    0.0551</td> <td>    0.008</td> <td>    6.605</td> <td> 0.000</td> <td>    0.039     0.071</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_BOEING CO</th>                             <td>    0.0230</td> <td>    0.007</td> <td>    3.524</td> <td> 0.000</td> <td>    0.010     0.036</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_BOOKING HOLDINGS INC</th>                  <td>    0.0600</td> <td>    0.011</td> <td>    5.539</td> <td> 0.000</td> <td>    0.039     0.081</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_BRISTOL MYERS SQUIBB COMPANY</th>          <td>    0.0405</td> <td>    0.007</td> <td>    5.456</td> <td> 0.000</td> <td>    0.026     0.055</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_BROADCOM CORP</th>                         <td>    0.0564</td> <td>    0.010</td> <td>    5.697</td> <td> 0.000</td> <td>    0.037     0.076</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_BROADCOM INC</th>                          <td>    0.0733</td> <td>    0.013</td> <td>    5.818</td> <td> 0.000</td> <td>    0.049     0.098</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_CATERPILLAR INC</th>                       <td>    0.0139</td> <td>    0.006</td> <td>    2.172</td> <td> 0.030</td> <td>    0.001     0.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_CELGENE CORP</th>                          <td>    0.0385</td> <td>    0.007</td> <td>    5.398</td> <td> 0.000</td> <td>    0.025     0.053</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_CHESAPEAKE ENERGY CORP</th>                <td>    0.0269</td> <td>    0.011</td> <td>    2.555</td> <td> 0.011</td> <td>    0.006     0.048</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_CHEVRON CORPORATION</th>                   <td>    0.0562</td> <td>    0.013</td> <td>    4.283</td> <td> 0.000</td> <td>    0.030     0.082</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_CISCO SYSTEMS INC</th>                     <td>    0.0285</td> <td>    0.006</td> <td>    4.464</td> <td> 0.000</td> <td>    0.016     0.041</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_CITIGROUP</th>                             <td>    0.1090</td> <td>    0.031</td> <td>    3.474</td> <td> 0.001</td> <td>    0.048     0.171</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_COCA-COLA CO</th>                          <td>    0.0384</td> <td>    0.009</td> <td>    4.352</td> <td> 0.000</td> <td>    0.021     0.056</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_COMCAST CORP</th>                          <td>    0.0325</td> <td>    0.006</td> <td>    5.155</td> <td> 0.000</td> <td>    0.020     0.045</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_CONOCOPHILLIPS</th>                        <td>    0.0456</td> <td>    0.011</td> <td>    4.089</td> <td> 0.000</td> <td>    0.024     0.067</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_COVIDIEN PLC</th>                          <td>    0.0743</td> <td>    0.012</td> <td>    6.354</td> <td> 0.000</td> <td>    0.051     0.097</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_CVS HEALTH CORP</th>                       <td>    0.0358</td> <td>    0.008</td> <td>    4.345</td> <td> 0.000</td> <td>    0.020     0.052</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_DEERE & CO</th>                            <td>    0.0115</td> <td>    0.007</td> <td>    1.683</td> <td> 0.092</td> <td>   -0.002     0.025</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_DELTA AIR LINES INC</th>                   <td>    0.0545</td> <td>    0.011</td> <td>    4.896</td> <td> 0.000</td> <td>    0.033     0.076</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_DIRECTV</th>                               <td>    0.0383</td> <td>    0.011</td> <td>    3.558</td> <td> 0.000</td> <td>    0.017     0.059</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_DOLLAR GENERAL CORP</th>                   <td>    0.0508</td> <td>    0.011</td> <td>    4.509</td> <td> 0.000</td> <td>    0.029     0.073</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_DOW CHEMICAL CO</th>                       <td>    0.0234</td> <td>    0.006</td> <td>    3.686</td> <td> 0.000</td> <td>    0.011     0.036</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_E.I. Du Pont De Nemours A</th>             <td>    0.0204</td> <td>    0.007</td> <td>    3.137</td> <td> 0.002</td> <td>    0.008     0.033</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_EBAY INC</th>                              <td>    0.0611</td> <td>    0.012</td> <td>    5.279</td> <td> 0.000</td> <td>    0.038     0.084</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_EMC CORPORATION</th>                       <td>    0.0341</td> <td>    0.007</td> <td>    5.245</td> <td> 0.000</td> <td>    0.021     0.047</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_EOG RESOURCES INC</th>                     <td>    0.0413</td> <td>    0.006</td> <td>    6.573</td> <td> 0.000</td> <td>    0.029     0.054</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_EXPRESS SCRIPTS HOLDING CO</th>            <td>    0.0177</td> <td>    0.007</td> <td>    2.628</td> <td> 0.009</td> <td>    0.005     0.031</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_EXXON MOBIL CORPORATION</th>               <td>    0.0677</td> <td>    0.015</td> <td>    4.484</td> <td> 0.000</td> <td>    0.038     0.097</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_FACEBOOK INC</th>                          <td>    0.0762</td> <td>    0.013</td> <td>    5.785</td> <td> 0.000</td> <td>    0.050     0.102</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_FEDEX CORPORATION</th>                     <td>    0.0321</td> <td>    0.007</td> <td>    4.657</td> <td> 0.000</td> <td>    0.019     0.046</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_FIRST SOLAR INC</th>                       <td>    0.0484</td> <td>    0.012</td> <td>    4.004</td> <td> 0.000</td> <td>    0.025     0.072</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_FORD MOTOR CO(NEW)</th>                    <td>    0.0213</td> <td>    0.008</td> <td>    2.647</td> <td> 0.008</td> <td>    0.006     0.037</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_FREEPORT-MCMORAN INC</th>                  <td>    0.0269</td> <td>    0.009</td> <td>    3.063</td> <td> 0.002</td> <td>    0.010     0.044</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_GENERAL ELECTRIC CO</th>                   <td>    0.0556</td> <td>    0.013</td> <td>    4.444</td> <td> 0.000</td> <td>    0.031     0.080</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_GENERAL MOTORS CO</th>                     <td>    0.0485</td> <td>    0.013</td> <td>    3.829</td> <td> 0.000</td> <td>    0.024     0.073</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_GILEAD SCIENCES INC</th>                   <td>    0.0459</td> <td>    0.008</td> <td>    5.868</td> <td> 0.000</td> <td>    0.031     0.061</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_GOLDMAN SACHS GROUP INC</th>               <td>    0.0965</td> <td>    0.017</td> <td>    5.644</td> <td> 0.000</td> <td>    0.063     0.130</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_GOPRO INC</th>                             <td>    0.0687</td> <td>    0.013</td> <td>    5.377</td> <td> 0.000</td> <td>    0.044     0.094</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_HALLIBURTON CO (HOLDING CO)</th>           <td>    0.0322</td> <td>    0.007</td> <td>    4.601</td> <td> 0.000</td> <td>    0.018     0.046</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_HOME DEPOT INC</th>                        <td>    0.0336</td> <td>    0.008</td> <td>    4.320</td> <td> 0.000</td> <td>    0.018     0.049</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_HP INC</th>                                <td>    0.0254</td> <td>    0.007</td> <td>    3.507</td> <td> 0.000</td> <td>    0.011     0.040</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_INTEL CORP</th>                            <td>    0.0383</td> <td>    0.008</td> <td>    4.891</td> <td> 0.000</td> <td>    0.023     0.054</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_INTL BUSINESS MACHINES CORP</th>           <td>    0.0329</td> <td>    0.009</td> <td>    3.784</td> <td> 0.000</td> <td>    0.016     0.050</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_JOHNSON AND JOHNSON</th>                   <td>    0.0431</td> <td>    0.009</td> <td>    4.833</td> <td> 0.000</td> <td>    0.026     0.061</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_JPMORGAN CHASE & CO COM STK</th>           <td>    0.1531</td> <td>    0.044</td> <td>    3.455</td> <td> 0.001</td> <td>    0.066     0.240</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_KEURIG GREEN MOUNTAIN INC</th>             <td>    0.0569</td> <td>    0.009</td> <td>    6.135</td> <td> 0.000</td> <td>    0.039     0.075</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_KINDER MORGAN INC</th>                     <td>    0.0427</td> <td>    0.012</td> <td>    3.497</td> <td> 0.000</td> <td>    0.019     0.067</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_LAS VEGAS SANDS CORP</th>                  <td>    0.0494</td> <td>    0.012</td> <td>    4.235</td> <td> 0.000</td> <td>    0.027     0.072</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_LILLY ELI & CO</th>                        <td>    0.0411</td> <td>    0.008</td> <td>    5.097</td> <td> 0.000</td> <td>    0.025     0.057</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_LINKEDIN CORP</th>                         <td>    0.0700</td> <td>    0.013</td> <td>    5.460</td> <td> 0.000</td> <td>    0.045     0.095</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_LOWES COMPANIES INC</th>                   <td>    0.0307</td> <td>    0.007</td> <td>    4.474</td> <td> 0.000</td> <td>    0.017     0.044</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_LYONDELLBASELL INDUSTRIES NV</th>          <td>    0.0478</td> <td>    0.012</td> <td>    4.043</td> <td> 0.000</td> <td>    0.025     0.071</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MARATHON PETROLEUM CORP</th>               <td>    0.0508</td> <td>    0.013</td> <td>    3.939</td> <td> 0.000</td> <td>    0.026     0.076</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MASTERCARD INCORPORATED</th>               <td>    0.0778</td> <td>    0.013</td> <td>    6.157</td> <td> 0.000</td> <td>    0.053     0.103</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MCDONALDS CORP</th>                        <td>    0.0380</td> <td>    0.009</td> <td>    4.236</td> <td> 0.000</td> <td>    0.020     0.056</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MEDTRONIC PLC</th>                         <td>    0.0482</td> <td>    0.008</td> <td>    6.017</td> <td> 0.000</td> <td>    0.033     0.064</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MERCK & CO INC</th>                        <td>    0.0425</td> <td>    0.008</td> <td>    5.002</td> <td> 0.000</td> <td>    0.026     0.059</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_METLIFE  INC</th>                          <td>    0.0738</td> <td>    0.018</td> <td>    4.162</td> <td> 0.000</td> <td>    0.039     0.109</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MICHAEL KORS HOLDINGS LTD</th>             <td>    0.0770</td> <td>    0.013</td> <td>    5.750</td> <td> 0.000</td> <td>    0.051     0.103</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MICRON TECHNOLOGY INC</th>                 <td>    0.0363</td> <td>    0.007</td> <td>    5.112</td> <td> 0.000</td> <td>    0.022     0.050</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MICROSOFT CORP</th>                        <td>    0.0468</td> <td>    0.009</td> <td>    5.410</td> <td> 0.000</td> <td>    0.030     0.064</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MONDELEZ INTERNATIONAL INC</th>            <td>    0.0519</td> <td>    0.011</td> <td>    4.934</td> <td> 0.000</td> <td>    0.031     0.073</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MONSANTO COMPANY</th>                      <td>    0.0556</td> <td>    0.011</td> <td>    5.111</td> <td> 0.000</td> <td>    0.034     0.077</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MORGAN STANLEY</th>                        <td>    0.0829</td> <td>    0.016</td> <td>    5.139</td> <td> 0.000</td> <td>    0.051     0.115</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MYLAN NV</th>                              <td>    0.0430</td> <td>    0.008</td> <td>    5.515</td> <td> 0.000</td> <td>    0.028     0.058</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_NATIONAL OILWELL VARCO  INC.</th>          <td>    0.0397</td> <td>    0.011</td> <td>    3.525</td> <td> 0.000</td> <td>    0.018     0.062</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_NETFLIX INC</th>                           <td>    0.0690</td> <td>    0.011</td> <td>    6.016</td> <td> 0.000</td> <td>    0.047     0.092</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_NEWMONT MINING CORP (HOLDING COMPANY)</th> <td>    0.0312</td> <td>    0.008</td> <td>    3.782</td> <td> 0.000</td> <td>    0.015     0.047</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_NEWS CP - CL A</th>                        <td>    0.0462</td> <td>    0.009</td> <td>    5.253</td> <td> 0.000</td> <td>    0.029     0.063</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_NIKE INC CL-B</th>                         <td>    0.0466</td> <td>    0.008</td> <td>    5.636</td> <td> 0.000</td> <td>    0.030     0.063</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_OCCIDENTAL PETROLEUM CORP</th>             <td>    0.0420</td> <td>    0.009</td> <td>    4.899</td> <td> 0.000</td> <td>    0.025     0.059</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_ORACLE CORP</th>                           <td>    0.0434</td> <td>    0.008</td> <td>    5.331</td> <td> 0.000</td> <td>    0.027     0.059</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_PANDORA MEDIA INC</th>                     <td>    0.0953</td> <td>    0.014</td> <td>    6.986</td> <td> 0.000</td> <td>    0.069     0.122</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_PENNEY J.C. CO INC (HOLDING COMPANY)</th>  <td>    0.0163</td> <td>    0.007</td> <td>    2.353</td> <td> 0.019</td> <td>    0.003     0.030</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_PEPSICO INC</th>                           <td>    0.0387</td> <td>    0.009</td> <td>    4.139</td> <td> 0.000</td> <td>    0.020     0.057</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_PFIZER INC</th>                            <td>    0.0498</td> <td>    0.009</td> <td>    5.350</td> <td> 0.000</td> <td>    0.032     0.068</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_PHILIP MORRIS INTERNATIONAL INC</th>       <td>    0.0577</td> <td>    0.014</td> <td>    4.236</td> <td> 0.000</td> <td>    0.031     0.084</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_PIONEER NAT RES CO</th>                    <td>    0.0716</td> <td>    0.010</td> <td>    7.136</td> <td> 0.000</td> <td>    0.052     0.091</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_PRECISION CASTPARTS CORP</th>              <td>    0.0510</td> <td>    0.009</td> <td>    5.448</td> <td> 0.000</td> <td>    0.033     0.069</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_PROCTER & GAMBLE CO</th>                   <td>    0.0442</td> <td>    0.010</td> <td>    4.581</td> <td> 0.000</td> <td>    0.025     0.063</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_QUALCOMM INC</th>                          <td>    0.0532</td> <td>    0.009</td> <td>    5.739</td> <td> 0.000</td> <td>    0.035     0.071</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_REGENERON PHARMACEUTICALS INC</th>         <td>    0.0537</td> <td>    0.011</td> <td>    4.675</td> <td> 0.000</td> <td>    0.031     0.076</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_SALESFORCE.COM INC</th>                    <td>    0.0610</td> <td>    0.011</td> <td>    5.303</td> <td> 0.000</td> <td>    0.038     0.083</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_SALIX PHARMACEUTICALS LTD</th>             <td> -4.65e-16</td> <td> 2.62e-15</td> <td>   -0.178</td> <td> 0.859</td> <td> -5.6e-15  4.67e-15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_SANDISK CORP</th>                          <td>    0.0624</td> <td>    0.010</td> <td>    6.426</td> <td> 0.000</td> <td>    0.043     0.081</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_SCHLUMBERGER LTD.</th>                     <td>    0.0441</td> <td>    0.009</td> <td>    4.843</td> <td> 0.000</td> <td>    0.026     0.062</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_SKYWORKS SOLUTIONS INC</th>                <td>    0.0772</td> <td>    0.011</td> <td>    6.749</td> <td> 0.000</td> <td>    0.055     0.100</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_SOLARCITY CORP</th>                        <td>    0.0820</td> <td>    0.013</td> <td>    6.129</td> <td> 0.000</td> <td>    0.056     0.108</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_SOUTHWEST AIRLINES CO</th>                 <td>    0.0430</td> <td>    0.007</td> <td>    5.909</td> <td> 0.000</td> <td>    0.029     0.057</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_STARBUCKS CORPORATION</th>                 <td>    0.0606</td> <td>    0.009</td> <td>    6.590</td> <td> 0.000</td> <td>    0.043     0.079</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_SUNEDISON INC</th>                         <td>    0.0234</td> <td>    0.017</td> <td>    1.401</td> <td> 0.161</td> <td>   -0.009     0.056</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_TARGET CORPORATION</th>                    <td>    0.0356</td> <td>    0.010</td> <td>    3.541</td> <td> 0.000</td> <td>    0.016     0.055</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_TESLA INC</th>                             <td>    0.0720</td> <td>    0.013</td> <td>    5.659</td> <td> 0.000</td> <td>    0.047     0.097</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_TEXAS INSTRUMENTS INC</th>                 <td>    0.0522</td> <td>    0.010</td> <td>    5.206</td> <td> 0.000</td> <td>    0.033     0.072</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_TIME WARNER CABLE INC</th>                 <td>    0.0468</td> <td>    0.011</td> <td>    4.218</td> <td> 0.000</td> <td>    0.025     0.069</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_TIME WARNER INC.</th>                      <td>    0.0227</td> <td>    0.005</td> <td>    4.748</td> <td> 0.000</td> <td>    0.013     0.032</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_TWITTER INC</th>                           <td>    0.0686</td> <td>    0.013</td> <td>    5.194</td> <td> 0.000</td> <td>    0.043     0.094</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_UNION PACIFIC CORPORATION</th>             <td>    0.0493</td> <td>    0.009</td> <td>    5.446</td> <td> 0.000</td> <td>    0.032     0.067</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_UNITED CONTINENTAL HOLDINGS IN</th>        <td>    0.0379</td> <td>    0.011</td> <td>    3.469</td> <td> 0.001</td> <td>    0.016     0.059</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_UNITED PARCEL SERVICE INC.CL B</th>        <td>    0.0366</td> <td>    0.011</td> <td>    3.462</td> <td> 0.001</td> <td>    0.016     0.057</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_UNITED TECHNOLOGIES CORP</th>              <td>    0.0390</td> <td>    0.009</td> <td>    4.277</td> <td> 0.000</td> <td>    0.021     0.057</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_UNITEDHEALTH GROUP INC</th>                <td>    0.0455</td> <td>    0.009</td> <td>    4.866</td> <td> 0.000</td> <td>    0.027     0.064</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_VALERO ENERGY CORP (NEW)</th>              <td>    0.0432</td> <td>    0.009</td> <td>    4.596</td> <td> 0.000</td> <td>    0.025     0.062</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_VERIZON COMMUNICATIONS</th>                <td>    0.0392</td> <td>    0.011</td> <td>    3.502</td> <td> 0.000</td> <td>    0.017     0.061</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_VISA INC</th>                              <td>    0.0769</td> <td>    0.012</td> <td>    6.389</td> <td> 0.000</td> <td>    0.053     0.100</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_WALGREENS BOOTS ALLIANCE INC</th>          <td>    0.0444</td> <td>    0.009</td> <td>    4.859</td> <td> 0.000</td> <td>    0.026     0.062</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_WALMART INC</th>                           <td>    0.0554</td> <td>    0.015</td> <td>    3.742</td> <td> 0.000</td> <td>    0.026     0.084</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_WALT DISNEY CO</th>                        <td>    0.0432</td> <td>    0.007</td> <td>    6.119</td> <td> 0.000</td> <td>    0.029     0.057</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_WELLS FARGO & CO(NEW)</th>                 <td>    0.1324</td> <td>    0.030</td> <td>    4.449</td> <td> 0.000</td> <td>    0.074     0.191</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_WILLIAMS COMPANIES</th>                    <td>    0.0472</td> <td>    0.009</td> <td>    5.143</td> <td> 0.000</td> <td>    0.029     0.065</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_WYNN RESORTS LTD</th>                      <td>    0.0410</td> <td>    0.011</td> <td>    3.710</td> <td> 0.000</td> <td>    0.019     0.063</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_YELP INC</th>                              <td>    0.0728</td> <td>    0.013</td> <td>    5.489</td> <td> 0.000</td> <td>    0.047     0.099</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>71.266</td> <th>  Durbin-Watson:     </th> <td>   1.433</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  77.090</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.067</td> <th>  Prob(JB):          </th> <td>1.82e-17</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.154</td> <th>  Cond. No.          </th> <td>3.42e+20</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:              Returns5D   R-squared:                       0.016\n",
       "Model:                            OLS   Adj. R-squared:                  0.012\n",
       "Method:                 Least Squares   F-statistic:                     4.124\n",
       "Date:                Sun, 09 Sep 2018   Prob (F-statistic):           2.28e-66\n",
       "Time:                        21:18:00   Log-Likelihood:                 95279.\n",
       "No. Observations:               44370   AIC:                        -1.902e+05\n",
       "Df Residuals:                   44195   BIC:                        -1.887e+05\n",
       "Df Model:                         174                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===============================================================================================================\n",
       "                                                  coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
       "---------------------------------------------------------------------------------------------------------------\n",
       "DividendYield                                4.504e-06   1.08e-06      4.185      0.000      2.39e-06  6.61e-06\n",
       "EBITDAYield                                  9.323e-06   2.17e-05      0.430      0.667     -3.32e-05  5.19e-05\n",
       "EVToEBITDA                                   1.425e-05   1.98e-05      0.719      0.472     -2.46e-05  5.31e-05\n",
       "EVToFCF                                      2.654e-05   2.47e-05      1.075      0.283     -2.19e-05  7.49e-05\n",
       "PriceToBook                                  1.582e-05   2.36e-05      0.669      0.503     -3.05e-05  6.21e-05\n",
       "PriceToDilutedEarningsTTM                    5.462e-07   1.06e-05      0.052      0.959     -2.01e-05  2.12e-05\n",
       "PriceToEarningsTTM                           1.449e-07   4.67e-07      0.310      0.757     -7.71e-07  1.06e-06\n",
       "PriceToFCF                                   -1.94e-05   2.07e-05     -0.939      0.348     -5.99e-05  2.11e-05\n",
       "PriceToOperatingCashflow                    -4.155e-05   1.42e-05     -2.934      0.003     -6.93e-05 -1.38e-05\n",
       "PriceToSalesTTM                             -8.975e-06   1.19e-06     -7.530      0.000     -1.13e-05 -6.64e-06\n",
       "Directional Movement Index                  -4.457e-06   4.75e-06     -0.938      0.348     -1.38e-05  4.86e-06\n",
       "Money Flow Index                            -5.894e-06   6.16e-06     -0.956      0.339      -1.8e-05  6.19e-06\n",
       "Percent Above Low                           -1.985e-05   1.18e-05     -1.675      0.094     -4.31e-05  3.38e-06\n",
       "Percent Below High                           7.651e-06   9.42e-06      0.812      0.417     -1.08e-05  2.61e-05\n",
       "Price Oscillator                             3.706e-06   6.64e-06      0.558      0.577     -9.31e-06  1.67e-05\n",
       "Trendline                                    1.414e-05   1.01e-05      1.402      0.161     -5.64e-06  3.39e-05\n",
       "AssetToEquityRatio                          -1.226e-07   8.91e-07     -0.138      0.891     -1.87e-06  1.62e-06\n",
       "AssetTurnover                                 -7.3e-06   3.95e-05     -0.185      0.854     -8.48e-05  7.02e-05\n",
       "CurrentRatio                                  4.11e-07   1.18e-06      0.347      0.728     -1.91e-06  2.73e-06\n",
       "DebtToAssetRatio                             2.484e-06   7.25e-07      3.427      0.001      1.06e-06   3.9e-06\n",
       "DebtToEquityRatio                           -5.365e-07   8.37e-07     -0.641      0.521     -2.18e-06   1.1e-06\n",
       "MertonsDD                                      -0.0004      0.000     -3.508      0.000        -0.001    -0.000\n",
       "WorkingCapitalToAssets                       1.341e-06   1.44e-06      0.932      0.351     -1.48e-06  4.16e-06\n",
       "WorkingCapitalToSales                       -3.118e-05   4.62e-05     -0.674      0.500        -0.000  5.95e-05\n",
       "Dividend Growth                             -2.629e-07    4.5e-07     -0.585      0.559     -1.14e-06  6.18e-07\n",
       "EPS                                          2.901e-07   7.04e-07      0.412      0.680     -1.09e-06  1.67e-06\n",
       "Net Debt                                    -8.116e-14   2.56e-14     -3.172      0.002     -1.31e-13  -3.1e-14\n",
       "Sales                                       -5.373e-14    2.4e-14     -2.239      0.025     -1.01e-13 -6.69e-15\n",
       "Total Assets                                -4.104e-14    1.8e-14     -2.276      0.023     -7.64e-14  -5.7e-15\n",
       "EPS Growth 3M                               -2.787e-06   1.08e-06     -2.582      0.010      -4.9e-06 -6.72e-07\n",
       "EPS Growth 12M                               1.257e-06   1.08e-06      1.161      0.246     -8.64e-07  3.38e-06\n",
       "Net Debt Growth 3M                           2.628e-07   1.37e-06      0.191      0.848     -2.43e-06  2.95e-06\n",
       "Net Debt Growth 12M                          2.592e-06    1.4e-06      1.858      0.063     -1.43e-07  5.33e-06\n",
       "Sales Growth 3M                              3.824e-06   1.45e-06      2.634      0.008      9.79e-07  6.67e-06\n",
       "Sales Growth 12M                             -2.38e-06   1.48e-06     -1.604      0.109     -5.29e-06  5.28e-07\n",
       "Total Assets Growth 3M                      -6.292e-07   1.63e-06     -0.386      0.699     -3.82e-06  2.56e-06\n",
       "Total Assets Growth 12M                      -1.46e-06   1.73e-06     -0.842      0.400     -4.86e-06  1.94e-06\n",
       "CFO To Assets                                4.634e-05   1.98e-05      2.342      0.019      7.56e-06  8.51e-05\n",
       "Capex To Assets                                -0.0001   4.26e-05     -3.232      0.001        -0.000 -5.42e-05\n",
       "Capex To FCF                                 3.673e-05   2.39e-05      1.534      0.125     -1.02e-05  8.37e-05\n",
       "Capex To Sales                                  0.0001   4.56e-05      2.247      0.025      1.31e-05     0.000\n",
       "EBIT To Assets                               8.146e-06      2e-05      0.408      0.683      -3.1e-05  4.73e-05\n",
       "Retained Earnings To Assets                    -0.0001    4.2e-05     -3.036      0.002        -0.000 -4.51e-05\n",
       "Downside Risk                                1.166e-05   1.22e-05      0.960      0.337     -1.22e-05  3.55e-05\n",
       "Index Beta                                  -7.363e-07   2.49e-07     -2.958      0.003     -1.22e-06 -2.48e-07\n",
       "Log Market Cap                               9.103e-05   4.24e-05      2.148      0.032      7.98e-06     0.000\n",
       "Volatility 3M                               -5.492e-05   1.04e-05     -5.306      0.000     -7.52e-05 -3.46e-05\n",
       "stock_3D SYSTEMS CORP                           0.0568      0.010      5.697      0.000         0.037     0.076\n",
       "stock_3M COMPANY                                0.0426      0.009      4.865      0.000         0.025     0.060\n",
       "stock_ABBOTT LABORATORIES                       0.0264      0.007      3.854      0.000         0.013     0.040\n",
       "stock_ABBVIE INC                                0.0572      0.013      4.482      0.000         0.032     0.082\n",
       "stock_ALLERGAN INC                              0.0487      0.007      7.349      0.000         0.036     0.062\n",
       "stock_ALLERGAN PLC                              0.0543      0.009      5.812      0.000         0.036     0.073\n",
       "stock_ALTABA INC                                0.0605      0.010      5.857      0.000         0.040     0.081\n",
       "stock_ALTRIA GROUP INC.                         0.0453      0.009      5.074      0.000         0.028     0.063\n",
       "stock_AMAZON.COM INC                            0.0513      0.010      4.905      0.000         0.031     0.072\n",
       "stock_AMERICAN AIRLINES GROUP INC               0.0490      0.012      4.079      0.000         0.025     0.073\n",
       "stock_AMERICAN EXPRESS COMPANY                  0.0232      0.006      4.177      0.000         0.012     0.034\n",
       "stock_AMERICAN INTL GROUP INC                   0.0452      0.009      4.770      0.000         0.027     0.064\n",
       "stock_AMGEN INC                                 0.0266      0.006      4.400      0.000         0.015     0.038\n",
       "stock_ANADARKO PETROLEUM CORP                   0.0347      0.005      6.387      0.000         0.024     0.045\n",
       "stock_APACHE CORP                               0.0130      0.009      1.487      0.137        -0.004     0.030\n",
       "stock_APPLE INC                                 0.0471      0.009      5.478      0.000         0.030     0.064\n",
       "stock_APPLIED MATERIALS INC                     0.0333      0.007      4.965      0.000         0.020     0.046\n",
       "stock_ARCONIC INC                               0.0090      0.005      1.822      0.068        -0.001     0.019\n",
       "stock_AT&T INC. COM                             0.0372      0.010      3.817      0.000         0.018     0.056\n",
       "stock_Alphabet Inc. Cl A                        0.0713      0.012      5.733      0.000         0.047     0.096\n",
       "stock_BAKER HUGHES INC                          0.0296      0.006      4.920      0.000         0.018     0.041\n",
       "stock_BANK OF AMERICA CORP                      0.1209      0.037      3.306      0.001         0.049     0.193\n",
       "stock_BERKSHIRE HATHAWAY INC CL-B               0.0629      0.013      4.939      0.000         0.038     0.088\n",
       "stock_BIOGEN INC                                0.0551      0.008      6.605      0.000         0.039     0.071\n",
       "stock_BOEING CO                                 0.0230      0.007      3.524      0.000         0.010     0.036\n",
       "stock_BOOKING HOLDINGS INC                      0.0600      0.011      5.539      0.000         0.039     0.081\n",
       "stock_BRISTOL MYERS SQUIBB COMPANY              0.0405      0.007      5.456      0.000         0.026     0.055\n",
       "stock_BROADCOM CORP                             0.0564      0.010      5.697      0.000         0.037     0.076\n",
       "stock_BROADCOM INC                              0.0733      0.013      5.818      0.000         0.049     0.098\n",
       "stock_CATERPILLAR INC                           0.0139      0.006      2.172      0.030         0.001     0.027\n",
       "stock_CELGENE CORP                              0.0385      0.007      5.398      0.000         0.025     0.053\n",
       "stock_CHESAPEAKE ENERGY CORP                    0.0269      0.011      2.555      0.011         0.006     0.048\n",
       "stock_CHEVRON CORPORATION                       0.0562      0.013      4.283      0.000         0.030     0.082\n",
       "stock_CISCO SYSTEMS INC                         0.0285      0.006      4.464      0.000         0.016     0.041\n",
       "stock_CITIGROUP                                 0.1090      0.031      3.474      0.001         0.048     0.171\n",
       "stock_COCA-COLA CO                              0.0384      0.009      4.352      0.000         0.021     0.056\n",
       "stock_COMCAST CORP                              0.0325      0.006      5.155      0.000         0.020     0.045\n",
       "stock_CONOCOPHILLIPS                            0.0456      0.011      4.089      0.000         0.024     0.067\n",
       "stock_COVIDIEN PLC                              0.0743      0.012      6.354      0.000         0.051     0.097\n",
       "stock_CVS HEALTH CORP                           0.0358      0.008      4.345      0.000         0.020     0.052\n",
       "stock_DEERE & CO                                0.0115      0.007      1.683      0.092        -0.002     0.025\n",
       "stock_DELTA AIR LINES INC                       0.0545      0.011      4.896      0.000         0.033     0.076\n",
       "stock_DIRECTV                                   0.0383      0.011      3.558      0.000         0.017     0.059\n",
       "stock_DOLLAR GENERAL CORP                       0.0508      0.011      4.509      0.000         0.029     0.073\n",
       "stock_DOW CHEMICAL CO                           0.0234      0.006      3.686      0.000         0.011     0.036\n",
       "stock_E.I. Du Pont De Nemours A                 0.0204      0.007      3.137      0.002         0.008     0.033\n",
       "stock_EBAY INC                                  0.0611      0.012      5.279      0.000         0.038     0.084\n",
       "stock_EMC CORPORATION                           0.0341      0.007      5.245      0.000         0.021     0.047\n",
       "stock_EOG RESOURCES INC                         0.0413      0.006      6.573      0.000         0.029     0.054\n",
       "stock_EXPRESS SCRIPTS HOLDING CO                0.0177      0.007      2.628      0.009         0.005     0.031\n",
       "stock_EXXON MOBIL CORPORATION                   0.0677      0.015      4.484      0.000         0.038     0.097\n",
       "stock_FACEBOOK INC                              0.0762      0.013      5.785      0.000         0.050     0.102\n",
       "stock_FEDEX CORPORATION                         0.0321      0.007      4.657      0.000         0.019     0.046\n",
       "stock_FIRST SOLAR INC                           0.0484      0.012      4.004      0.000         0.025     0.072\n",
       "stock_FORD MOTOR CO(NEW)                        0.0213      0.008      2.647      0.008         0.006     0.037\n",
       "stock_FREEPORT-MCMORAN INC                      0.0269      0.009      3.063      0.002         0.010     0.044\n",
       "stock_GENERAL ELECTRIC CO                       0.0556      0.013      4.444      0.000         0.031     0.080\n",
       "stock_GENERAL MOTORS CO                         0.0485      0.013      3.829      0.000         0.024     0.073\n",
       "stock_GILEAD SCIENCES INC                       0.0459      0.008      5.868      0.000         0.031     0.061\n",
       "stock_GOLDMAN SACHS GROUP INC                   0.0965      0.017      5.644      0.000         0.063     0.130\n",
       "stock_GOPRO INC                                 0.0687      0.013      5.377      0.000         0.044     0.094\n",
       "stock_HALLIBURTON CO (HOLDING CO)               0.0322      0.007      4.601      0.000         0.018     0.046\n",
       "stock_HOME DEPOT INC                            0.0336      0.008      4.320      0.000         0.018     0.049\n",
       "stock_HP INC                                    0.0254      0.007      3.507      0.000         0.011     0.040\n",
       "stock_INTEL CORP                                0.0383      0.008      4.891      0.000         0.023     0.054\n",
       "stock_INTL BUSINESS MACHINES CORP               0.0329      0.009      3.784      0.000         0.016     0.050\n",
       "stock_JOHNSON AND JOHNSON                       0.0431      0.009      4.833      0.000         0.026     0.061\n",
       "stock_JPMORGAN CHASE & CO COM STK               0.1531      0.044      3.455      0.001         0.066     0.240\n",
       "stock_KEURIG GREEN MOUNTAIN INC                 0.0569      0.009      6.135      0.000         0.039     0.075\n",
       "stock_KINDER MORGAN INC                         0.0427      0.012      3.497      0.000         0.019     0.067\n",
       "stock_LAS VEGAS SANDS CORP                      0.0494      0.012      4.235      0.000         0.027     0.072\n",
       "stock_LILLY ELI & CO                            0.0411      0.008      5.097      0.000         0.025     0.057\n",
       "stock_LINKEDIN CORP                             0.0700      0.013      5.460      0.000         0.045     0.095\n",
       "stock_LOWES COMPANIES INC                       0.0307      0.007      4.474      0.000         0.017     0.044\n",
       "stock_LYONDELLBASELL INDUSTRIES NV              0.0478      0.012      4.043      0.000         0.025     0.071\n",
       "stock_MARATHON PETROLEUM CORP                   0.0508      0.013      3.939      0.000         0.026     0.076\n",
       "stock_MASTERCARD INCORPORATED                   0.0778      0.013      6.157      0.000         0.053     0.103\n",
       "stock_MCDONALDS CORP                            0.0380      0.009      4.236      0.000         0.020     0.056\n",
       "stock_MEDTRONIC PLC                             0.0482      0.008      6.017      0.000         0.033     0.064\n",
       "stock_MERCK & CO INC                            0.0425      0.008      5.002      0.000         0.026     0.059\n",
       "stock_METLIFE  INC                              0.0738      0.018      4.162      0.000         0.039     0.109\n",
       "stock_MICHAEL KORS HOLDINGS LTD                 0.0770      0.013      5.750      0.000         0.051     0.103\n",
       "stock_MICRON TECHNOLOGY INC                     0.0363      0.007      5.112      0.000         0.022     0.050\n",
       "stock_MICROSOFT CORP                            0.0468      0.009      5.410      0.000         0.030     0.064\n",
       "stock_MONDELEZ INTERNATIONAL INC                0.0519      0.011      4.934      0.000         0.031     0.073\n",
       "stock_MONSANTO COMPANY                          0.0556      0.011      5.111      0.000         0.034     0.077\n",
       "stock_MORGAN STANLEY                            0.0829      0.016      5.139      0.000         0.051     0.115\n",
       "stock_MYLAN NV                                  0.0430      0.008      5.515      0.000         0.028     0.058\n",
       "stock_NATIONAL OILWELL VARCO  INC.              0.0397      0.011      3.525      0.000         0.018     0.062\n",
       "stock_NETFLIX INC                               0.0690      0.011      6.016      0.000         0.047     0.092\n",
       "stock_NEWMONT MINING CORP (HOLDING COMPANY)     0.0312      0.008      3.782      0.000         0.015     0.047\n",
       "stock_NEWS CP - CL A                            0.0462      0.009      5.253      0.000         0.029     0.063\n",
       "stock_NIKE INC CL-B                             0.0466      0.008      5.636      0.000         0.030     0.063\n",
       "stock_OCCIDENTAL PETROLEUM CORP                 0.0420      0.009      4.899      0.000         0.025     0.059\n",
       "stock_ORACLE CORP                               0.0434      0.008      5.331      0.000         0.027     0.059\n",
       "stock_PANDORA MEDIA INC                         0.0953      0.014      6.986      0.000         0.069     0.122\n",
       "stock_PENNEY J.C. CO INC (HOLDING COMPANY)      0.0163      0.007      2.353      0.019         0.003     0.030\n",
       "stock_PEPSICO INC                               0.0387      0.009      4.139      0.000         0.020     0.057\n",
       "stock_PFIZER INC                                0.0498      0.009      5.350      0.000         0.032     0.068\n",
       "stock_PHILIP MORRIS INTERNATIONAL INC           0.0577      0.014      4.236      0.000         0.031     0.084\n",
       "stock_PIONEER NAT RES CO                        0.0716      0.010      7.136      0.000         0.052     0.091\n",
       "stock_PRECISION CASTPARTS CORP                  0.0510      0.009      5.448      0.000         0.033     0.069\n",
       "stock_PROCTER & GAMBLE CO                       0.0442      0.010      4.581      0.000         0.025     0.063\n",
       "stock_QUALCOMM INC                              0.0532      0.009      5.739      0.000         0.035     0.071\n",
       "stock_REGENERON PHARMACEUTICALS INC             0.0537      0.011      4.675      0.000         0.031     0.076\n",
       "stock_SALESFORCE.COM INC                        0.0610      0.011      5.303      0.000         0.038     0.083\n",
       "stock_SALIX PHARMACEUTICALS LTD              -4.65e-16   2.62e-15     -0.178      0.859      -5.6e-15  4.67e-15\n",
       "stock_SANDISK CORP                              0.0624      0.010      6.426      0.000         0.043     0.081\n",
       "stock_SCHLUMBERGER LTD.                         0.0441      0.009      4.843      0.000         0.026     0.062\n",
       "stock_SKYWORKS SOLUTIONS INC                    0.0772      0.011      6.749      0.000         0.055     0.100\n",
       "stock_SOLARCITY CORP                            0.0820      0.013      6.129      0.000         0.056     0.108\n",
       "stock_SOUTHWEST AIRLINES CO                     0.0430      0.007      5.909      0.000         0.029     0.057\n",
       "stock_STARBUCKS CORPORATION                     0.0606      0.009      6.590      0.000         0.043     0.079\n",
       "stock_SUNEDISON INC                             0.0234      0.017      1.401      0.161        -0.009     0.056\n",
       "stock_TARGET CORPORATION                        0.0356      0.010      3.541      0.000         0.016     0.055\n",
       "stock_TESLA INC                                 0.0720      0.013      5.659      0.000         0.047     0.097\n",
       "stock_TEXAS INSTRUMENTS INC                     0.0522      0.010      5.206      0.000         0.033     0.072\n",
       "stock_TIME WARNER CABLE INC                     0.0468      0.011      4.218      0.000         0.025     0.069\n",
       "stock_TIME WARNER INC.                          0.0227      0.005      4.748      0.000         0.013     0.032\n",
       "stock_TWITTER INC                               0.0686      0.013      5.194      0.000         0.043     0.094\n",
       "stock_UNION PACIFIC CORPORATION                 0.0493      0.009      5.446      0.000         0.032     0.067\n",
       "stock_UNITED CONTINENTAL HOLDINGS IN            0.0379      0.011      3.469      0.001         0.016     0.059\n",
       "stock_UNITED PARCEL SERVICE INC.CL B            0.0366      0.011      3.462      0.001         0.016     0.057\n",
       "stock_UNITED TECHNOLOGIES CORP                  0.0390      0.009      4.277      0.000         0.021     0.057\n",
       "stock_UNITEDHEALTH GROUP INC                    0.0455      0.009      4.866      0.000         0.027     0.064\n",
       "stock_VALERO ENERGY CORP (NEW)                  0.0432      0.009      4.596      0.000         0.025     0.062\n",
       "stock_VERIZON COMMUNICATIONS                    0.0392      0.011      3.502      0.000         0.017     0.061\n",
       "stock_VISA INC                                  0.0769      0.012      6.389      0.000         0.053     0.100\n",
       "stock_WALGREENS BOOTS ALLIANCE INC              0.0444      0.009      4.859      0.000         0.026     0.062\n",
       "stock_WALMART INC                               0.0554      0.015      3.742      0.000         0.026     0.084\n",
       "stock_WALT DISNEY CO                            0.0432      0.007      6.119      0.000         0.029     0.057\n",
       "stock_WELLS FARGO & CO(NEW)                     0.1324      0.030      4.449      0.000         0.074     0.191\n",
       "stock_WILLIAMS COMPANIES                        0.0472      0.009      5.143      0.000         0.029     0.065\n",
       "stock_WYNN RESORTS LTD                          0.0410      0.011      3.710      0.000         0.019     0.063\n",
       "stock_YELP INC                                  0.0728      0.013      5.489      0.000         0.047     0.099\n",
       "==============================================================================\n",
       "Omnibus:                       71.266   Durbin-Watson:                   1.433\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               77.090\n",
       "Skew:                          -0.067   Prob(JB):                     1.82e-17\n",
       "Kurtosis:                       3.154   Cond. No.                     3.42e+20\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The smallest eigenvalue is 3.44e-14. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target = 'Returns5D'\n",
    "model_data = pd.concat([y[[target]], X], axis=1).dropna()\n",
    "model_data = model_data[model_data[target].between(model_data[target].quantile(.025), \n",
    "                                                   model_data[target].quantile(.975))]\n",
    "\n",
    "model = OLS(endog=model_data[target], exog=model_data.drop(target, axis=1))\n",
    "trained_model = model.fit()\n",
    "trained_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Returns10D</td>    <th>  R-squared:         </th>  <td>   0.035</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.031</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   9.045</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 09 Sep 2018</td> <th>  Prob (F-statistic):</th>  <td>5.42e-219</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:18:02</td>     <th>  Log-Likelihood:    </th>  <td>  78861.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 43734</td>      <th>  AIC:               </th> <td>-1.574e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 43559</td>      <th>  BIC:               </th> <td>-1.559e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>   174</td>      <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                       <td></td>                          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>DividendYield</th>                               <td> 6.757e-06</td> <td> 1.55e-06</td> <td>    4.370</td> <td> 0.000</td> <td> 3.73e-06  9.79e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>EBITDAYield</th>                                 <td>-1.021e-05</td> <td> 3.11e-05</td> <td>   -0.329</td> <td> 0.743</td> <td>-7.11e-05  5.07e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>EVToEBITDA</th>                                  <td> 9.163e-05</td> <td> 2.83e-05</td> <td>    3.233</td> <td> 0.001</td> <td> 3.61e-05     0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>EVToFCF</th>                                     <td> 2.372e-05</td> <td> 3.51e-05</td> <td>    0.676</td> <td> 0.499</td> <td> -4.5e-05  9.25e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PriceToBook</th>                                 <td> 8.448e-05</td> <td> 3.34e-05</td> <td>    2.533</td> <td> 0.011</td> <td> 1.91e-05     0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PriceToDilutedEarningsTTM</th>                   <td>-1.332e-05</td> <td>  1.5e-05</td> <td>   -0.889</td> <td> 0.374</td> <td>-4.27e-05  1.61e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PriceToEarningsTTM</th>                          <td>-6.983e-07</td> <td> 6.67e-07</td> <td>   -1.047</td> <td> 0.295</td> <td>   -2e-06  6.08e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PriceToFCF</th>                                  <td>-4.525e-06</td> <td> 2.91e-05</td> <td>   -0.155</td> <td> 0.877</td> <td>-6.16e-05  5.26e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PriceToOperatingCashflow</th>                    <td>-8.243e-05</td> <td> 2.02e-05</td> <td>   -4.071</td> <td> 0.000</td> <td>   -0.000 -4.27e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PriceToSalesTTM</th>                             <td>-2.324e-05</td> <td> 1.72e-06</td> <td>  -13.493</td> <td> 0.000</td> <td>-2.66e-05 -1.99e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Directional Movement Index</th>                  <td>-2.235e-05</td> <td> 6.75e-06</td> <td>   -3.308</td> <td> 0.001</td> <td>-3.56e-05 -9.11e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Money Flow Index</th>                            <td> 4.299e-06</td> <td> 8.77e-06</td> <td>    0.490</td> <td> 0.624</td> <td>-1.29e-05  2.15e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Percent Above Low</th>                           <td>-4.475e-05</td> <td> 1.71e-05</td> <td>   -2.623</td> <td> 0.009</td> <td>-7.82e-05 -1.13e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Percent Below High</th>                          <td> 1.295e-05</td> <td> 1.34e-05</td> <td>    0.965</td> <td> 0.335</td> <td>-1.34e-05  3.93e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Price Oscillator</th>                            <td>  6.06e-06</td> <td> 9.48e-06</td> <td>    0.640</td> <td> 0.522</td> <td>-1.25e-05  2.46e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Trendline</th>                                   <td>  2.48e-05</td> <td> 1.44e-05</td> <td>    1.722</td> <td> 0.085</td> <td>-3.42e-06   5.3e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AssetToEquityRatio</th>                          <td>-2.548e-06</td> <td>  1.3e-06</td> <td>   -1.964</td> <td> 0.049</td> <td>-5.09e-06 -5.59e-09</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AssetTurnover</th>                               <td>   -0.0001</td> <td> 5.63e-05</td> <td>   -2.054</td> <td> 0.040</td> <td>   -0.000 -5.31e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CurrentRatio</th>                                <td> 3.392e-06</td> <td> 1.69e-06</td> <td>    2.007</td> <td> 0.045</td> <td> 7.96e-08   6.7e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>DebtToAssetRatio</th>                            <td> 2.605e-06</td> <td> 1.04e-06</td> <td>    2.500</td> <td> 0.012</td> <td> 5.63e-07  4.65e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>DebtToEquityRatio</th>                           <td>-2.576e-08</td> <td> 1.21e-06</td> <td>   -0.021</td> <td> 0.983</td> <td> -2.4e-06  2.35e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MertonsDD</th>                                   <td>   -0.0010</td> <td>    0.000</td> <td>   -6.752</td> <td> 0.000</td> <td>   -0.001    -0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WorkingCapitalToAssets</th>                      <td>  3.97e-06</td> <td> 2.04e-06</td> <td>    1.943</td> <td> 0.052</td> <td>-3.38e-08  7.97e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WorkingCapitalToSales</th>                       <td>   -0.0002</td> <td> 6.58e-05</td> <td>   -2.654</td> <td> 0.008</td> <td>   -0.000 -4.56e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dividend Growth</th>                             <td>-9.485e-07</td> <td> 6.45e-07</td> <td>   -1.471</td> <td> 0.141</td> <td>-2.21e-06  3.16e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>EPS</th>                                         <td> 5.254e-07</td> <td>    1e-06</td> <td>    0.525</td> <td> 0.599</td> <td>-1.43e-06  2.48e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Net Debt</th>                                    <td> -1.53e-13</td> <td> 3.65e-14</td> <td>   -4.193</td> <td> 0.000</td> <td>-2.25e-13 -8.15e-14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sales</th>                                       <td>-1.061e-13</td> <td> 3.43e-14</td> <td>   -3.094</td> <td> 0.002</td> <td>-1.73e-13 -3.89e-14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Total Assets</th>                                <td>-7.923e-14</td> <td> 2.58e-14</td> <td>   -3.068</td> <td> 0.002</td> <td> -1.3e-13 -2.86e-14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>EPS Growth 3M</th>                               <td>-6.126e-06</td> <td> 1.53e-06</td> <td>   -4.002</td> <td> 0.000</td> <td>-9.13e-06 -3.13e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>EPS Growth 12M</th>                              <td> 2.182e-06</td> <td> 1.53e-06</td> <td>    1.423</td> <td> 0.155</td> <td>-8.24e-07  5.19e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Net Debt Growth 3M</th>                          <td>-1.748e-06</td> <td> 1.95e-06</td> <td>   -0.897</td> <td> 0.370</td> <td>-5.57e-06  2.07e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Net Debt Growth 12M</th>                         <td> 5.675e-06</td> <td> 1.98e-06</td> <td>    2.872</td> <td> 0.004</td> <td>  1.8e-06  9.55e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sales Growth 3M</th>                             <td>  4.22e-06</td> <td> 2.06e-06</td> <td>    2.051</td> <td> 0.040</td> <td> 1.87e-07  8.25e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sales Growth 12M</th>                            <td> -6.66e-06</td> <td>  2.1e-06</td> <td>   -3.169</td> <td> 0.002</td> <td>-1.08e-05 -2.54e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Total Assets Growth 3M</th>                      <td>-2.435e-07</td> <td> 2.31e-06</td> <td>   -0.105</td> <td> 0.916</td> <td>-4.77e-06  4.28e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Total Assets Growth 12M</th>                     <td>-2.601e-06</td> <td> 2.46e-06</td> <td>   -1.059</td> <td> 0.290</td> <td>-7.42e-06  2.21e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CFO To Assets</th>                               <td> 4.355e-05</td> <td> 2.81e-05</td> <td>    1.551</td> <td> 0.121</td> <td>-1.15e-05  9.86e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Capex To Assets</th>                             <td>   -0.0001</td> <td>  6.1e-05</td> <td>   -2.246</td> <td> 0.025</td> <td>   -0.000 -1.74e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Capex To FCF</th>                                <td>  6.95e-05</td> <td> 3.42e-05</td> <td>    2.030</td> <td> 0.042</td> <td>  2.4e-06     0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Capex To Sales</th>                              <td>    0.0002</td> <td> 6.51e-05</td> <td>    3.016</td> <td> 0.003</td> <td> 6.87e-05     0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>EBIT To Assets</th>                              <td> 1.444e-05</td> <td> 2.83e-05</td> <td>    0.511</td> <td> 0.610</td> <td> -4.1e-05  6.99e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Retained Earnings To Assets</th>                 <td>   -0.0004</td> <td> 6.01e-05</td> <td>   -6.676</td> <td> 0.000</td> <td>   -0.001    -0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Downside Risk</th>                               <td> 3.345e-05</td> <td> 1.73e-05</td> <td>    1.932</td> <td> 0.053</td> <td>-4.83e-07  6.74e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Index Beta</th>                                  <td> -2.09e-06</td> <td> 3.55e-07</td> <td>   -5.886</td> <td> 0.000</td> <td>-2.79e-06 -1.39e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Log Market Cap</th>                              <td>    0.0002</td> <td> 6.04e-05</td> <td>    3.147</td> <td> 0.002</td> <td> 7.17e-05     0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Volatility 3M</th>                               <td>-8.754e-05</td> <td> 1.47e-05</td> <td>   -5.939</td> <td> 0.000</td> <td>   -0.000 -5.86e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_3D SYSTEMS CORP</th>                       <td>    0.1609</td> <td>    0.014</td> <td>   11.264</td> <td> 0.000</td> <td>    0.133     0.189</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_3M COMPANY</th>                            <td>    0.1423</td> <td>    0.012</td> <td>   11.416</td> <td> 0.000</td> <td>    0.118     0.167</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_ABBOTT LABORATORIES</th>                   <td>    0.0903</td> <td>    0.010</td> <td>    9.116</td> <td> 0.000</td> <td>    0.071     0.110</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_ABBVIE INC</th>                            <td>    0.1833</td> <td>    0.018</td> <td>   10.070</td> <td> 0.000</td> <td>    0.148     0.219</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_ALLERGAN INC</th>                          <td>    0.1434</td> <td>    0.009</td> <td>   15.113</td> <td> 0.000</td> <td>    0.125     0.162</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_ALLERGAN PLC</th>                          <td>    0.1683</td> <td>    0.013</td> <td>   12.648</td> <td> 0.000</td> <td>    0.142     0.194</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_ALTABA INC</th>                            <td>    0.1825</td> <td>    0.015</td> <td>   12.388</td> <td> 0.000</td> <td>    0.154     0.211</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_ALTRIA GROUP INC.</th>                     <td>    0.1533</td> <td>    0.013</td> <td>   12.015</td> <td> 0.000</td> <td>    0.128     0.178</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_AMAZON.COM INC</th>                        <td>    0.1494</td> <td>    0.015</td> <td>   10.035</td> <td> 0.000</td> <td>    0.120     0.179</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_AMERICAN AIRLINES GROUP INC</th>           <td>    0.1433</td> <td>    0.017</td> <td>    8.373</td> <td> 0.000</td> <td>    0.110     0.177</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_AMERICAN EXPRESS COMPANY</th>              <td>    0.0785</td> <td>    0.008</td> <td>    9.844</td> <td> 0.000</td> <td>    0.063     0.094</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_AMERICAN INTL GROUP INC</th>               <td>    0.1021</td> <td>    0.014</td> <td>    7.505</td> <td> 0.000</td> <td>    0.075     0.129</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_AMGEN INC</th>                             <td>    0.0915</td> <td>    0.009</td> <td>   10.550</td> <td> 0.000</td> <td>    0.074     0.108</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_ANADARKO PETROLEUM CORP</th>               <td>    0.0978</td> <td>    0.008</td> <td>   12.627</td> <td> 0.000</td> <td>    0.083     0.113</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_APACHE CORP</th>                           <td>    0.0568</td> <td>    0.014</td> <td>    4.038</td> <td> 0.000</td> <td>    0.029     0.084</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_APPLE INC</th>                             <td>    0.1309</td> <td>    0.012</td> <td>   10.689</td> <td> 0.000</td> <td>    0.107     0.155</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_APPLIED MATERIALS INC</th>                 <td>    0.1080</td> <td>    0.010</td> <td>   11.289</td> <td> 0.000</td> <td>    0.089     0.127</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_ARCONIC INC</th>                           <td>    0.0422</td> <td>    0.007</td> <td>    5.984</td> <td> 0.000</td> <td>    0.028     0.056</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_AT&T INC. COM</th>                         <td>    0.1122</td> <td>    0.014</td> <td>    8.068</td> <td> 0.000</td> <td>    0.085     0.139</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_Alphabet Inc. Cl A</th>                    <td>    0.2024</td> <td>    0.018</td> <td>   11.450</td> <td> 0.000</td> <td>    0.168     0.237</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_BAKER HUGHES INC</th>                      <td>    0.0945</td> <td>    0.009</td> <td>   11.076</td> <td> 0.000</td> <td>    0.078     0.111</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_BANK OF AMERICA CORP</th>                  <td>    0.2669</td> <td>    0.052</td> <td>    5.090</td> <td> 0.000</td> <td>    0.164     0.370</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_BERKSHIRE HATHAWAY INC CL-B</th>           <td>    0.1630</td> <td>    0.018</td> <td>    8.968</td> <td> 0.000</td> <td>    0.127     0.199</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_BIOGEN INC</th>                            <td>    0.1688</td> <td>    0.012</td> <td>   14.185</td> <td> 0.000</td> <td>    0.145     0.192</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_BOEING CO</th>                             <td>    0.0769</td> <td>    0.009</td> <td>    8.299</td> <td> 0.000</td> <td>    0.059     0.095</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_BOOKING HOLDINGS INC</th>                  <td>    0.1898</td> <td>    0.015</td> <td>   12.281</td> <td> 0.000</td> <td>    0.159     0.220</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_BRISTOL MYERS SQUIBB COMPANY</th>          <td>    0.1327</td> <td>    0.011</td> <td>   12.504</td> <td> 0.000</td> <td>    0.112     0.153</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_BROADCOM CORP</th>                         <td>    0.1655</td> <td>    0.014</td> <td>   11.708</td> <td> 0.000</td> <td>    0.138     0.193</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_BROADCOM INC</th>                          <td>    0.2090</td> <td>    0.018</td> <td>   11.611</td> <td> 0.000</td> <td>    0.174     0.244</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_CATERPILLAR INC</th>                       <td>    0.0647</td> <td>    0.009</td> <td>    7.093</td> <td> 0.000</td> <td>    0.047     0.083</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_CELGENE CORP</th>                          <td>    0.1326</td> <td>    0.010</td> <td>   12.974</td> <td> 0.000</td> <td>    0.113     0.153</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_CHESAPEAKE ENERGY CORP</th>                <td>    0.0682</td> <td>    0.016</td> <td>    4.174</td> <td> 0.000</td> <td>    0.036     0.100</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_CHEVRON CORPORATION</th>                   <td>    0.1637</td> <td>    0.019</td> <td>    8.770</td> <td> 0.000</td> <td>    0.127     0.200</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_CISCO SYSTEMS INC</th>                     <td>    0.0975</td> <td>    0.009</td> <td>   10.689</td> <td> 0.000</td> <td>    0.080     0.115</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_CITIGROUP</th>                             <td>    0.2358</td> <td>    0.045</td> <td>    5.236</td> <td> 0.000</td> <td>    0.148     0.324</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_COCA-COLA CO</th>                          <td>    0.1313</td> <td>    0.013</td> <td>   10.439</td> <td> 0.000</td> <td>    0.107     0.156</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_COMCAST CORP</th>                          <td>    0.0961</td> <td>    0.009</td> <td>   10.691</td> <td> 0.000</td> <td>    0.078     0.114</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_CONOCOPHILLIPS</th>                        <td>    0.1432</td> <td>    0.016</td> <td>    9.039</td> <td> 0.000</td> <td>    0.112     0.174</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_COVIDIEN PLC</th>                          <td>    0.2103</td> <td>    0.017</td> <td>   12.561</td> <td> 0.000</td> <td>    0.177     0.243</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_CVS HEALTH CORP</th>                       <td>    0.1147</td> <td>    0.012</td> <td>    9.823</td> <td> 0.000</td> <td>    0.092     0.138</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_DEERE & CO</th>                            <td>    0.0556</td> <td>    0.010</td> <td>    5.698</td> <td> 0.000</td> <td>    0.036     0.075</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_DELTA AIR LINES INC</th>                   <td>    0.1560</td> <td>    0.016</td> <td>    9.833</td> <td> 0.000</td> <td>    0.125     0.187</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_DIRECTV</th>                               <td>    0.1262</td> <td>    0.015</td> <td>    8.223</td> <td> 0.000</td> <td>    0.096     0.156</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_DOLLAR GENERAL CORP</th>                   <td>    0.1543</td> <td>    0.016</td> <td>    9.600</td> <td> 0.000</td> <td>    0.123     0.186</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_DOW CHEMICAL CO</th>                       <td>    0.0810</td> <td>    0.009</td> <td>    8.981</td> <td> 0.000</td> <td>    0.063     0.099</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_E.I. Du Pont De Nemours A</th>             <td>    0.0722</td> <td>    0.009</td> <td>    7.789</td> <td> 0.000</td> <td>    0.054     0.090</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_EBAY INC</th>                              <td>    0.1879</td> <td>    0.016</td> <td>   11.397</td> <td> 0.000</td> <td>    0.156     0.220</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_EMC CORPORATION</th>                       <td>    0.1141</td> <td>    0.009</td> <td>   12.320</td> <td> 0.000</td> <td>    0.096     0.132</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_EOG RESOURCES INC</th>                     <td>    0.1212</td> <td>    0.009</td> <td>   13.575</td> <td> 0.000</td> <td>    0.104     0.139</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_EXPRESS SCRIPTS HOLDING CO</th>            <td>    0.0745</td> <td>    0.010</td> <td>    7.775</td> <td> 0.000</td> <td>    0.056     0.093</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_EXXON MOBIL CORPORATION</th>               <td>    0.1865</td> <td>    0.021</td> <td>    8.679</td> <td> 0.000</td> <td>    0.144     0.229</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_FACEBOOK INC</th>                          <td>    0.2171</td> <td>    0.019</td> <td>   11.586</td> <td> 0.000</td> <td>    0.180     0.254</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_FEDEX CORPORATION</th>                     <td>    0.1002</td> <td>    0.010</td> <td>   10.231</td> <td> 0.000</td> <td>    0.081     0.119</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_FIRST SOLAR INC</th>                       <td>    0.1560</td> <td>    0.017</td> <td>    9.046</td> <td> 0.000</td> <td>    0.122     0.190</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_FORD MOTOR CO(NEW)</th>                    <td>    0.0733</td> <td>    0.011</td> <td>    6.384</td> <td> 0.000</td> <td>    0.051     0.096</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_FREEPORT-MCMORAN INC</th>                  <td>    0.0974</td> <td>    0.013</td> <td>    7.766</td> <td> 0.000</td> <td>    0.073     0.122</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_GENERAL ELECTRIC CO</th>                   <td>    0.1500</td> <td>    0.018</td> <td>    8.332</td> <td> 0.000</td> <td>    0.115     0.185</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_GENERAL MOTORS CO</th>                     <td>    0.1448</td> <td>    0.018</td> <td>    8.032</td> <td> 0.000</td> <td>    0.109     0.180</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_GILEAD SCIENCES INC</th>                   <td>    0.1445</td> <td>    0.011</td> <td>   12.934</td> <td> 0.000</td> <td>    0.123     0.166</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_GOLDMAN SACHS GROUP INC</th>               <td>    0.2410</td> <td>    0.025</td> <td>    9.822</td> <td> 0.000</td> <td>    0.193     0.289</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_GOPRO INC</th>                             <td>    0.1946</td> <td>    0.018</td> <td>   10.636</td> <td> 0.000</td> <td>    0.159     0.230</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_HALLIBURTON CO (HOLDING CO)</th>           <td>    0.1050</td> <td>    0.010</td> <td>   10.582</td> <td> 0.000</td> <td>    0.086     0.124</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_HOME DEPOT INC</th>                        <td>    0.1160</td> <td>    0.011</td> <td>   10.522</td> <td> 0.000</td> <td>    0.094     0.138</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_HP INC</th>                                <td>    0.0879</td> <td>    0.010</td> <td>    8.554</td> <td> 0.000</td> <td>    0.068     0.108</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_INTEL CORP</th>                            <td>    0.1170</td> <td>    0.011</td> <td>   10.511</td> <td> 0.000</td> <td>    0.095     0.139</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_INTL BUSINESS MACHINES CORP</th>           <td>    0.1135</td> <td>    0.012</td> <td>    9.176</td> <td> 0.000</td> <td>    0.089     0.138</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_JOHNSON AND JOHNSON</th>                   <td>    0.1375</td> <td>    0.013</td> <td>   10.821</td> <td> 0.000</td> <td>    0.113     0.162</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_JPMORGAN CHASE & CO COM STK</th>           <td>    0.3443</td> <td>    0.064</td> <td>    5.416</td> <td> 0.000</td> <td>    0.220     0.469</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_KEURIG GREEN MOUNTAIN INC</th>             <td>    0.1655</td> <td>    0.013</td> <td>   12.506</td> <td> 0.000</td> <td>    0.140     0.191</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_KINDER MORGAN INC</th>                     <td>    0.1372</td> <td>    0.017</td> <td>    7.888</td> <td> 0.000</td> <td>    0.103     0.171</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_LAS VEGAS SANDS CORP</th>                  <td>    0.1657</td> <td>    0.017</td> <td>    9.963</td> <td> 0.000</td> <td>    0.133     0.198</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_LILLY ELI & CO</th>                        <td>    0.1296</td> <td>    0.011</td> <td>   11.275</td> <td> 0.000</td> <td>    0.107     0.152</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_LINKEDIN CORP</th>                         <td>    0.1981</td> <td>    0.018</td> <td>   10.815</td> <td> 0.000</td> <td>    0.162     0.234</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_LOWES COMPANIES INC</th>                   <td>    0.1054</td> <td>    0.010</td> <td>   10.827</td> <td> 0.000</td> <td>    0.086     0.125</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_LYONDELLBASELL INDUSTRIES NV</th>          <td>    0.1509</td> <td>    0.017</td> <td>    8.959</td> <td> 0.000</td> <td>    0.118     0.184</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MARATHON PETROLEUM CORP</th>               <td>    0.1477</td> <td>    0.018</td> <td>    8.006</td> <td> 0.000</td> <td>    0.112     0.184</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MASTERCARD INCORPORATED</th>               <td>    0.2324</td> <td>    0.018</td> <td>   12.916</td> <td> 0.000</td> <td>    0.197     0.268</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MCDONALDS CORP</th>                        <td>    0.1322</td> <td>    0.013</td> <td>   10.363</td> <td> 0.000</td> <td>    0.107     0.157</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MEDTRONIC PLC</th>                         <td>    0.1496</td> <td>    0.011</td> <td>   13.103</td> <td> 0.000</td> <td>    0.127     0.172</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MERCK & CO INC</th>                        <td>    0.1372</td> <td>    0.012</td> <td>   11.332</td> <td> 0.000</td> <td>    0.113     0.161</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_METLIFE  INC</th>                          <td>    0.1720</td> <td>    0.025</td> <td>    6.765</td> <td> 0.000</td> <td>    0.122     0.222</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MICHAEL KORS HOLDINGS LTD</th>             <td>    0.2136</td> <td>    0.019</td> <td>   11.178</td> <td> 0.000</td> <td>    0.176     0.251</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MICRON TECHNOLOGY INC</th>                 <td>    0.1122</td> <td>    0.010</td> <td>   11.094</td> <td> 0.000</td> <td>    0.092     0.132</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MICROSOFT CORP</th>                        <td>    0.1369</td> <td>    0.012</td> <td>   11.104</td> <td> 0.000</td> <td>    0.113     0.161</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MONDELEZ INTERNATIONAL INC</th>            <td>    0.1580</td> <td>    0.015</td> <td>   10.549</td> <td> 0.000</td> <td>    0.129     0.187</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MONSANTO COMPANY</th>                      <td>    0.1725</td> <td>    0.015</td> <td>   11.144</td> <td> 0.000</td> <td>    0.142     0.203</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MORGAN STANLEY</th>                        <td>    0.2094</td> <td>    0.023</td> <td>    9.058</td> <td> 0.000</td> <td>    0.164     0.255</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MYLAN NV</th>                              <td>    0.1362</td> <td>    0.011</td> <td>   12.231</td> <td> 0.000</td> <td>    0.114     0.158</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_NATIONAL OILWELL VARCO  INC.</th>          <td>    0.1420</td> <td>    0.016</td> <td>    8.817</td> <td> 0.000</td> <td>    0.110     0.174</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_NETFLIX INC</th>                           <td>    0.2052</td> <td>    0.016</td> <td>   12.512</td> <td> 0.000</td> <td>    0.173     0.237</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_NEWMONT MINING CORP (HOLDING COMPANY)</th> <td>    0.0949</td> <td>    0.012</td> <td>    7.982</td> <td> 0.000</td> <td>    0.072     0.118</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_NEWS CP - CL A</th>                        <td>    0.1394</td> <td>    0.013</td> <td>   11.131</td> <td> 0.000</td> <td>    0.115     0.164</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_NIKE INC CL-B</th>                         <td>    0.1440</td> <td>    0.012</td> <td>   12.236</td> <td> 0.000</td> <td>    0.121     0.167</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_OCCIDENTAL PETROLEUM CORP</th>             <td>    0.1303</td> <td>    0.012</td> <td>   10.690</td> <td> 0.000</td> <td>    0.106     0.154</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_ORACLE CORP</th>                           <td>    0.1384</td> <td>    0.012</td> <td>   11.935</td> <td> 0.000</td> <td>    0.116     0.161</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_PANDORA MEDIA INC</th>                     <td>    0.2348</td> <td>    0.020</td> <td>   11.961</td> <td> 0.000</td> <td>    0.196     0.273</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_PENNEY J.C. CO INC (HOLDING COMPANY)</th>  <td>    0.0553</td> <td>    0.010</td> <td>    5.558</td> <td> 0.000</td> <td>    0.036     0.075</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_PEPSICO INC</th>                           <td>    0.1325</td> <td>    0.013</td> <td>    9.962</td> <td> 0.000</td> <td>    0.106     0.159</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_PFIZER INC</th>                            <td>    0.1554</td> <td>    0.013</td> <td>   11.701</td> <td> 0.000</td> <td>    0.129     0.181</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_PHILIP MORRIS INTERNATIONAL INC</th>       <td>    0.2017</td> <td>    0.019</td> <td>   10.405</td> <td> 0.000</td> <td>    0.164     0.240</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_PIONEER NAT RES CO</th>                    <td>    0.1980</td> <td>    0.014</td> <td>   13.850</td> <td> 0.000</td> <td>    0.170     0.226</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_PRECISION CASTPARTS CORP</th>              <td>    0.1519</td> <td>    0.014</td> <td>   11.190</td> <td> 0.000</td> <td>    0.125     0.179</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_PROCTER & GAMBLE CO</th>                   <td>    0.1412</td> <td>    0.014</td> <td>   10.286</td> <td> 0.000</td> <td>    0.114     0.168</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_QUALCOMM INC</th>                          <td>    0.1585</td> <td>    0.013</td> <td>   12.011</td> <td> 0.000</td> <td>    0.133     0.184</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_REGENERON PHARMACEUTICALS INC</th>         <td>    0.1549</td> <td>    0.018</td> <td>    8.846</td> <td> 0.000</td> <td>    0.121     0.189</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_SALESFORCE.COM INC</th>                    <td>    0.1797</td> <td>    0.016</td> <td>   10.945</td> <td> 0.000</td> <td>    0.148     0.212</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_SALIX PHARMACEUTICALS LTD</th>             <td> 1.482e-11</td> <td> 1.92e-11</td> <td>    0.771</td> <td> 0.440</td> <td>-2.28e-11  5.25e-11</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_SANDISK CORP</th>                          <td>    0.1842</td> <td>    0.014</td> <td>   13.280</td> <td> 0.000</td> <td>    0.157     0.211</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_SCHLUMBERGER LTD.</th>                     <td>    0.1437</td> <td>    0.013</td> <td>   11.126</td> <td> 0.000</td> <td>    0.118     0.169</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_SKYWORKS SOLUTIONS INC</th>                <td>    0.1829</td> <td>    0.016</td> <td>   11.217</td> <td> 0.000</td> <td>    0.151     0.215</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_SOLARCITY CORP</th>                        <td>    0.2218</td> <td>    0.019</td> <td>   11.593</td> <td> 0.000</td> <td>    0.184     0.259</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_SOUTHWEST AIRLINES CO</th>                 <td>    0.1205</td> <td>    0.010</td> <td>   11.615</td> <td> 0.000</td> <td>    0.100     0.141</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_STARBUCKS CORPORATION</th>                 <td>    0.1770</td> <td>    0.013</td> <td>   13.501</td> <td> 0.000</td> <td>    0.151     0.203</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_SUNEDISON INC</th>                         <td>    0.1297</td> <td>    0.042</td> <td>    3.095</td> <td> 0.002</td> <td>    0.048     0.212</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_TARGET CORPORATION</th>                    <td>    0.1226</td> <td>    0.014</td> <td>    8.556</td> <td> 0.000</td> <td>    0.094     0.151</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_TESLA INC</th>                             <td>    0.2039</td> <td>    0.018</td> <td>   11.215</td> <td> 0.000</td> <td>    0.168     0.240</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_TEXAS INSTRUMENTS INC</th>                 <td>    0.1730</td> <td>    0.014</td> <td>   12.080</td> <td> 0.000</td> <td>    0.145     0.201</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_TIME WARNER CABLE INC</th>                 <td>    0.1446</td> <td>    0.016</td> <td>    9.138</td> <td> 0.000</td> <td>    0.114     0.176</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_TIME WARNER INC.</th>                      <td>    0.0757</td> <td>    0.007</td> <td>   11.026</td> <td> 0.000</td> <td>    0.062     0.089</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_TWITTER INC</th>                           <td>    0.1978</td> <td>    0.019</td> <td>   10.476</td> <td> 0.000</td> <td>    0.161     0.235</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_UNION PACIFIC CORPORATION</th>             <td>    0.1502</td> <td>    0.013</td> <td>   11.655</td> <td> 0.000</td> <td>    0.125     0.175</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_UNITED CONTINENTAL HOLDINGS IN</th>        <td>    0.1245</td> <td>    0.016</td> <td>    7.991</td> <td> 0.000</td> <td>    0.094     0.155</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_UNITED PARCEL SERVICE INC.CL B</th>        <td>    0.1248</td> <td>    0.015</td> <td>    8.288</td> <td> 0.000</td> <td>    0.095     0.154</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_UNITED TECHNOLOGIES CORP</th>              <td>    0.1288</td> <td>    0.013</td> <td>    9.937</td> <td> 0.000</td> <td>    0.103     0.154</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_UNITEDHEALTH GROUP INC</th>                <td>    0.1402</td> <td>    0.013</td> <td>   10.559</td> <td> 0.000</td> <td>    0.114     0.166</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_VALERO ENERGY CORP (NEW)</th>              <td>    0.1296</td> <td>    0.013</td> <td>    9.686</td> <td> 0.000</td> <td>    0.103     0.156</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_VERIZON COMMUNICATIONS</th>                <td>    0.1198</td> <td>    0.016</td> <td>    7.496</td> <td> 0.000</td> <td>    0.088     0.151</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_VISA INC</th>                              <td>    0.2206</td> <td>    0.017</td> <td>   12.867</td> <td> 0.000</td> <td>    0.187     0.254</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_WALGREENS BOOTS ALLIANCE INC</th>          <td>    0.1369</td> <td>    0.013</td> <td>   10.559</td> <td> 0.000</td> <td>    0.111     0.162</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_WALMART INC</th>                           <td>    0.1522</td> <td>    0.021</td> <td>    7.225</td> <td> 0.000</td> <td>    0.111     0.194</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_WALT DISNEY CO</th>                        <td>    0.1309</td> <td>    0.010</td> <td>   13.023</td> <td> 0.000</td> <td>    0.111     0.151</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_WELLS FARGO & CO(NEW)</th>                 <td>    0.2969</td> <td>    0.043</td> <td>    6.946</td> <td> 0.000</td> <td>    0.213     0.381</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_WILLIAMS COMPANIES</th>                    <td>    0.1325</td> <td>    0.013</td> <td>   10.104</td> <td> 0.000</td> <td>    0.107     0.158</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_WYNN RESORTS LTD</th>                      <td>    0.1418</td> <td>    0.016</td> <td>    8.999</td> <td> 0.000</td> <td>    0.111     0.173</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_YELP INC</th>                              <td>    0.1947</td> <td>    0.019</td> <td>   10.257</td> <td> 0.000</td> <td>    0.157     0.232</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.654</td> <th>  Durbin-Watson:     </th> <td>   1.503</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.721</td> <th>  Jarque-Bera (JB):  </th> <td>   0.669</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.007</td> <th>  Prob(JB):          </th> <td>   0.716</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.986</td> <th>  Cond. No.          </th> <td>6.39e+16</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             Returns10D   R-squared:                       0.035\n",
       "Model:                            OLS   Adj. R-squared:                  0.031\n",
       "Method:                 Least Squares   F-statistic:                     9.045\n",
       "Date:                Sun, 09 Sep 2018   Prob (F-statistic):          5.42e-219\n",
       "Time:                        21:18:02   Log-Likelihood:                 78861.\n",
       "No. Observations:               43734   AIC:                        -1.574e+05\n",
       "Df Residuals:                   43559   BIC:                        -1.559e+05\n",
       "Df Model:                         174                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===============================================================================================================\n",
       "                                                  coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
       "---------------------------------------------------------------------------------------------------------------\n",
       "DividendYield                                6.757e-06   1.55e-06      4.370      0.000      3.73e-06  9.79e-06\n",
       "EBITDAYield                                 -1.021e-05   3.11e-05     -0.329      0.743     -7.11e-05  5.07e-05\n",
       "EVToEBITDA                                   9.163e-05   2.83e-05      3.233      0.001      3.61e-05     0.000\n",
       "EVToFCF                                      2.372e-05   3.51e-05      0.676      0.499      -4.5e-05  9.25e-05\n",
       "PriceToBook                                  8.448e-05   3.34e-05      2.533      0.011      1.91e-05     0.000\n",
       "PriceToDilutedEarningsTTM                   -1.332e-05    1.5e-05     -0.889      0.374     -4.27e-05  1.61e-05\n",
       "PriceToEarningsTTM                          -6.983e-07   6.67e-07     -1.047      0.295        -2e-06  6.08e-07\n",
       "PriceToFCF                                  -4.525e-06   2.91e-05     -0.155      0.877     -6.16e-05  5.26e-05\n",
       "PriceToOperatingCashflow                    -8.243e-05   2.02e-05     -4.071      0.000        -0.000 -4.27e-05\n",
       "PriceToSalesTTM                             -2.324e-05   1.72e-06    -13.493      0.000     -2.66e-05 -1.99e-05\n",
       "Directional Movement Index                  -2.235e-05   6.75e-06     -3.308      0.001     -3.56e-05 -9.11e-06\n",
       "Money Flow Index                             4.299e-06   8.77e-06      0.490      0.624     -1.29e-05  2.15e-05\n",
       "Percent Above Low                           -4.475e-05   1.71e-05     -2.623      0.009     -7.82e-05 -1.13e-05\n",
       "Percent Below High                           1.295e-05   1.34e-05      0.965      0.335     -1.34e-05  3.93e-05\n",
       "Price Oscillator                              6.06e-06   9.48e-06      0.640      0.522     -1.25e-05  2.46e-05\n",
       "Trendline                                     2.48e-05   1.44e-05      1.722      0.085     -3.42e-06   5.3e-05\n",
       "AssetToEquityRatio                          -2.548e-06    1.3e-06     -1.964      0.049     -5.09e-06 -5.59e-09\n",
       "AssetTurnover                                  -0.0001   5.63e-05     -2.054      0.040        -0.000 -5.31e-06\n",
       "CurrentRatio                                 3.392e-06   1.69e-06      2.007      0.045      7.96e-08   6.7e-06\n",
       "DebtToAssetRatio                             2.605e-06   1.04e-06      2.500      0.012      5.63e-07  4.65e-06\n",
       "DebtToEquityRatio                           -2.576e-08   1.21e-06     -0.021      0.983      -2.4e-06  2.35e-06\n",
       "MertonsDD                                      -0.0010      0.000     -6.752      0.000        -0.001    -0.001\n",
       "WorkingCapitalToAssets                        3.97e-06   2.04e-06      1.943      0.052     -3.38e-08  7.97e-06\n",
       "WorkingCapitalToSales                          -0.0002   6.58e-05     -2.654      0.008        -0.000 -4.56e-05\n",
       "Dividend Growth                             -9.485e-07   6.45e-07     -1.471      0.141     -2.21e-06  3.16e-07\n",
       "EPS                                          5.254e-07      1e-06      0.525      0.599     -1.43e-06  2.48e-06\n",
       "Net Debt                                     -1.53e-13   3.65e-14     -4.193      0.000     -2.25e-13 -8.15e-14\n",
       "Sales                                       -1.061e-13   3.43e-14     -3.094      0.002     -1.73e-13 -3.89e-14\n",
       "Total Assets                                -7.923e-14   2.58e-14     -3.068      0.002      -1.3e-13 -2.86e-14\n",
       "EPS Growth 3M                               -6.126e-06   1.53e-06     -4.002      0.000     -9.13e-06 -3.13e-06\n",
       "EPS Growth 12M                               2.182e-06   1.53e-06      1.423      0.155     -8.24e-07  5.19e-06\n",
       "Net Debt Growth 3M                          -1.748e-06   1.95e-06     -0.897      0.370     -5.57e-06  2.07e-06\n",
       "Net Debt Growth 12M                          5.675e-06   1.98e-06      2.872      0.004       1.8e-06  9.55e-06\n",
       "Sales Growth 3M                               4.22e-06   2.06e-06      2.051      0.040      1.87e-07  8.25e-06\n",
       "Sales Growth 12M                             -6.66e-06    2.1e-06     -3.169      0.002     -1.08e-05 -2.54e-06\n",
       "Total Assets Growth 3M                      -2.435e-07   2.31e-06     -0.105      0.916     -4.77e-06  4.28e-06\n",
       "Total Assets Growth 12M                     -2.601e-06   2.46e-06     -1.059      0.290     -7.42e-06  2.21e-06\n",
       "CFO To Assets                                4.355e-05   2.81e-05      1.551      0.121     -1.15e-05  9.86e-05\n",
       "Capex To Assets                                -0.0001    6.1e-05     -2.246      0.025        -0.000 -1.74e-05\n",
       "Capex To FCF                                  6.95e-05   3.42e-05      2.030      0.042       2.4e-06     0.000\n",
       "Capex To Sales                                  0.0002   6.51e-05      3.016      0.003      6.87e-05     0.000\n",
       "EBIT To Assets                               1.444e-05   2.83e-05      0.511      0.610      -4.1e-05  6.99e-05\n",
       "Retained Earnings To Assets                    -0.0004   6.01e-05     -6.676      0.000        -0.001    -0.000\n",
       "Downside Risk                                3.345e-05   1.73e-05      1.932      0.053     -4.83e-07  6.74e-05\n",
       "Index Beta                                   -2.09e-06   3.55e-07     -5.886      0.000     -2.79e-06 -1.39e-06\n",
       "Log Market Cap                                  0.0002   6.04e-05      3.147      0.002      7.17e-05     0.000\n",
       "Volatility 3M                               -8.754e-05   1.47e-05     -5.939      0.000        -0.000 -5.86e-05\n",
       "stock_3D SYSTEMS CORP                           0.1609      0.014     11.264      0.000         0.133     0.189\n",
       "stock_3M COMPANY                                0.1423      0.012     11.416      0.000         0.118     0.167\n",
       "stock_ABBOTT LABORATORIES                       0.0903      0.010      9.116      0.000         0.071     0.110\n",
       "stock_ABBVIE INC                                0.1833      0.018     10.070      0.000         0.148     0.219\n",
       "stock_ALLERGAN INC                              0.1434      0.009     15.113      0.000         0.125     0.162\n",
       "stock_ALLERGAN PLC                              0.1683      0.013     12.648      0.000         0.142     0.194\n",
       "stock_ALTABA INC                                0.1825      0.015     12.388      0.000         0.154     0.211\n",
       "stock_ALTRIA GROUP INC.                         0.1533      0.013     12.015      0.000         0.128     0.178\n",
       "stock_AMAZON.COM INC                            0.1494      0.015     10.035      0.000         0.120     0.179\n",
       "stock_AMERICAN AIRLINES GROUP INC               0.1433      0.017      8.373      0.000         0.110     0.177\n",
       "stock_AMERICAN EXPRESS COMPANY                  0.0785      0.008      9.844      0.000         0.063     0.094\n",
       "stock_AMERICAN INTL GROUP INC                   0.1021      0.014      7.505      0.000         0.075     0.129\n",
       "stock_AMGEN INC                                 0.0915      0.009     10.550      0.000         0.074     0.108\n",
       "stock_ANADARKO PETROLEUM CORP                   0.0978      0.008     12.627      0.000         0.083     0.113\n",
       "stock_APACHE CORP                               0.0568      0.014      4.038      0.000         0.029     0.084\n",
       "stock_APPLE INC                                 0.1309      0.012     10.689      0.000         0.107     0.155\n",
       "stock_APPLIED MATERIALS INC                     0.1080      0.010     11.289      0.000         0.089     0.127\n",
       "stock_ARCONIC INC                               0.0422      0.007      5.984      0.000         0.028     0.056\n",
       "stock_AT&T INC. COM                             0.1122      0.014      8.068      0.000         0.085     0.139\n",
       "stock_Alphabet Inc. Cl A                        0.2024      0.018     11.450      0.000         0.168     0.237\n",
       "stock_BAKER HUGHES INC                          0.0945      0.009     11.076      0.000         0.078     0.111\n",
       "stock_BANK OF AMERICA CORP                      0.2669      0.052      5.090      0.000         0.164     0.370\n",
       "stock_BERKSHIRE HATHAWAY INC CL-B               0.1630      0.018      8.968      0.000         0.127     0.199\n",
       "stock_BIOGEN INC                                0.1688      0.012     14.185      0.000         0.145     0.192\n",
       "stock_BOEING CO                                 0.0769      0.009      8.299      0.000         0.059     0.095\n",
       "stock_BOOKING HOLDINGS INC                      0.1898      0.015     12.281      0.000         0.159     0.220\n",
       "stock_BRISTOL MYERS SQUIBB COMPANY              0.1327      0.011     12.504      0.000         0.112     0.153\n",
       "stock_BROADCOM CORP                             0.1655      0.014     11.708      0.000         0.138     0.193\n",
       "stock_BROADCOM INC                              0.2090      0.018     11.611      0.000         0.174     0.244\n",
       "stock_CATERPILLAR INC                           0.0647      0.009      7.093      0.000         0.047     0.083\n",
       "stock_CELGENE CORP                              0.1326      0.010     12.974      0.000         0.113     0.153\n",
       "stock_CHESAPEAKE ENERGY CORP                    0.0682      0.016      4.174      0.000         0.036     0.100\n",
       "stock_CHEVRON CORPORATION                       0.1637      0.019      8.770      0.000         0.127     0.200\n",
       "stock_CISCO SYSTEMS INC                         0.0975      0.009     10.689      0.000         0.080     0.115\n",
       "stock_CITIGROUP                                 0.2358      0.045      5.236      0.000         0.148     0.324\n",
       "stock_COCA-COLA CO                              0.1313      0.013     10.439      0.000         0.107     0.156\n",
       "stock_COMCAST CORP                              0.0961      0.009     10.691      0.000         0.078     0.114\n",
       "stock_CONOCOPHILLIPS                            0.1432      0.016      9.039      0.000         0.112     0.174\n",
       "stock_COVIDIEN PLC                              0.2103      0.017     12.561      0.000         0.177     0.243\n",
       "stock_CVS HEALTH CORP                           0.1147      0.012      9.823      0.000         0.092     0.138\n",
       "stock_DEERE & CO                                0.0556      0.010      5.698      0.000         0.036     0.075\n",
       "stock_DELTA AIR LINES INC                       0.1560      0.016      9.833      0.000         0.125     0.187\n",
       "stock_DIRECTV                                   0.1262      0.015      8.223      0.000         0.096     0.156\n",
       "stock_DOLLAR GENERAL CORP                       0.1543      0.016      9.600      0.000         0.123     0.186\n",
       "stock_DOW CHEMICAL CO                           0.0810      0.009      8.981      0.000         0.063     0.099\n",
       "stock_E.I. Du Pont De Nemours A                 0.0722      0.009      7.789      0.000         0.054     0.090\n",
       "stock_EBAY INC                                  0.1879      0.016     11.397      0.000         0.156     0.220\n",
       "stock_EMC CORPORATION                           0.1141      0.009     12.320      0.000         0.096     0.132\n",
       "stock_EOG RESOURCES INC                         0.1212      0.009     13.575      0.000         0.104     0.139\n",
       "stock_EXPRESS SCRIPTS HOLDING CO                0.0745      0.010      7.775      0.000         0.056     0.093\n",
       "stock_EXXON MOBIL CORPORATION                   0.1865      0.021      8.679      0.000         0.144     0.229\n",
       "stock_FACEBOOK INC                              0.2171      0.019     11.586      0.000         0.180     0.254\n",
       "stock_FEDEX CORPORATION                         0.1002      0.010     10.231      0.000         0.081     0.119\n",
       "stock_FIRST SOLAR INC                           0.1560      0.017      9.046      0.000         0.122     0.190\n",
       "stock_FORD MOTOR CO(NEW)                        0.0733      0.011      6.384      0.000         0.051     0.096\n",
       "stock_FREEPORT-MCMORAN INC                      0.0974      0.013      7.766      0.000         0.073     0.122\n",
       "stock_GENERAL ELECTRIC CO                       0.1500      0.018      8.332      0.000         0.115     0.185\n",
       "stock_GENERAL MOTORS CO                         0.1448      0.018      8.032      0.000         0.109     0.180\n",
       "stock_GILEAD SCIENCES INC                       0.1445      0.011     12.934      0.000         0.123     0.166\n",
       "stock_GOLDMAN SACHS GROUP INC                   0.2410      0.025      9.822      0.000         0.193     0.289\n",
       "stock_GOPRO INC                                 0.1946      0.018     10.636      0.000         0.159     0.230\n",
       "stock_HALLIBURTON CO (HOLDING CO)               0.1050      0.010     10.582      0.000         0.086     0.124\n",
       "stock_HOME DEPOT INC                            0.1160      0.011     10.522      0.000         0.094     0.138\n",
       "stock_HP INC                                    0.0879      0.010      8.554      0.000         0.068     0.108\n",
       "stock_INTEL CORP                                0.1170      0.011     10.511      0.000         0.095     0.139\n",
       "stock_INTL BUSINESS MACHINES CORP               0.1135      0.012      9.176      0.000         0.089     0.138\n",
       "stock_JOHNSON AND JOHNSON                       0.1375      0.013     10.821      0.000         0.113     0.162\n",
       "stock_JPMORGAN CHASE & CO COM STK               0.3443      0.064      5.416      0.000         0.220     0.469\n",
       "stock_KEURIG GREEN MOUNTAIN INC                 0.1655      0.013     12.506      0.000         0.140     0.191\n",
       "stock_KINDER MORGAN INC                         0.1372      0.017      7.888      0.000         0.103     0.171\n",
       "stock_LAS VEGAS SANDS CORP                      0.1657      0.017      9.963      0.000         0.133     0.198\n",
       "stock_LILLY ELI & CO                            0.1296      0.011     11.275      0.000         0.107     0.152\n",
       "stock_LINKEDIN CORP                             0.1981      0.018     10.815      0.000         0.162     0.234\n",
       "stock_LOWES COMPANIES INC                       0.1054      0.010     10.827      0.000         0.086     0.125\n",
       "stock_LYONDELLBASELL INDUSTRIES NV              0.1509      0.017      8.959      0.000         0.118     0.184\n",
       "stock_MARATHON PETROLEUM CORP                   0.1477      0.018      8.006      0.000         0.112     0.184\n",
       "stock_MASTERCARD INCORPORATED                   0.2324      0.018     12.916      0.000         0.197     0.268\n",
       "stock_MCDONALDS CORP                            0.1322      0.013     10.363      0.000         0.107     0.157\n",
       "stock_MEDTRONIC PLC                             0.1496      0.011     13.103      0.000         0.127     0.172\n",
       "stock_MERCK & CO INC                            0.1372      0.012     11.332      0.000         0.113     0.161\n",
       "stock_METLIFE  INC                              0.1720      0.025      6.765      0.000         0.122     0.222\n",
       "stock_MICHAEL KORS HOLDINGS LTD                 0.2136      0.019     11.178      0.000         0.176     0.251\n",
       "stock_MICRON TECHNOLOGY INC                     0.1122      0.010     11.094      0.000         0.092     0.132\n",
       "stock_MICROSOFT CORP                            0.1369      0.012     11.104      0.000         0.113     0.161\n",
       "stock_MONDELEZ INTERNATIONAL INC                0.1580      0.015     10.549      0.000         0.129     0.187\n",
       "stock_MONSANTO COMPANY                          0.1725      0.015     11.144      0.000         0.142     0.203\n",
       "stock_MORGAN STANLEY                            0.2094      0.023      9.058      0.000         0.164     0.255\n",
       "stock_MYLAN NV                                  0.1362      0.011     12.231      0.000         0.114     0.158\n",
       "stock_NATIONAL OILWELL VARCO  INC.              0.1420      0.016      8.817      0.000         0.110     0.174\n",
       "stock_NETFLIX INC                               0.2052      0.016     12.512      0.000         0.173     0.237\n",
       "stock_NEWMONT MINING CORP (HOLDING COMPANY)     0.0949      0.012      7.982      0.000         0.072     0.118\n",
       "stock_NEWS CP - CL A                            0.1394      0.013     11.131      0.000         0.115     0.164\n",
       "stock_NIKE INC CL-B                             0.1440      0.012     12.236      0.000         0.121     0.167\n",
       "stock_OCCIDENTAL PETROLEUM CORP                 0.1303      0.012     10.690      0.000         0.106     0.154\n",
       "stock_ORACLE CORP                               0.1384      0.012     11.935      0.000         0.116     0.161\n",
       "stock_PANDORA MEDIA INC                         0.2348      0.020     11.961      0.000         0.196     0.273\n",
       "stock_PENNEY J.C. CO INC (HOLDING COMPANY)      0.0553      0.010      5.558      0.000         0.036     0.075\n",
       "stock_PEPSICO INC                               0.1325      0.013      9.962      0.000         0.106     0.159\n",
       "stock_PFIZER INC                                0.1554      0.013     11.701      0.000         0.129     0.181\n",
       "stock_PHILIP MORRIS INTERNATIONAL INC           0.2017      0.019     10.405      0.000         0.164     0.240\n",
       "stock_PIONEER NAT RES CO                        0.1980      0.014     13.850      0.000         0.170     0.226\n",
       "stock_PRECISION CASTPARTS CORP                  0.1519      0.014     11.190      0.000         0.125     0.179\n",
       "stock_PROCTER & GAMBLE CO                       0.1412      0.014     10.286      0.000         0.114     0.168\n",
       "stock_QUALCOMM INC                              0.1585      0.013     12.011      0.000         0.133     0.184\n",
       "stock_REGENERON PHARMACEUTICALS INC             0.1549      0.018      8.846      0.000         0.121     0.189\n",
       "stock_SALESFORCE.COM INC                        0.1797      0.016     10.945      0.000         0.148     0.212\n",
       "stock_SALIX PHARMACEUTICALS LTD              1.482e-11   1.92e-11      0.771      0.440     -2.28e-11  5.25e-11\n",
       "stock_SANDISK CORP                              0.1842      0.014     13.280      0.000         0.157     0.211\n",
       "stock_SCHLUMBERGER LTD.                         0.1437      0.013     11.126      0.000         0.118     0.169\n",
       "stock_SKYWORKS SOLUTIONS INC                    0.1829      0.016     11.217      0.000         0.151     0.215\n",
       "stock_SOLARCITY CORP                            0.2218      0.019     11.593      0.000         0.184     0.259\n",
       "stock_SOUTHWEST AIRLINES CO                     0.1205      0.010     11.615      0.000         0.100     0.141\n",
       "stock_STARBUCKS CORPORATION                     0.1770      0.013     13.501      0.000         0.151     0.203\n",
       "stock_SUNEDISON INC                             0.1297      0.042      3.095      0.002         0.048     0.212\n",
       "stock_TARGET CORPORATION                        0.1226      0.014      8.556      0.000         0.094     0.151\n",
       "stock_TESLA INC                                 0.2039      0.018     11.215      0.000         0.168     0.240\n",
       "stock_TEXAS INSTRUMENTS INC                     0.1730      0.014     12.080      0.000         0.145     0.201\n",
       "stock_TIME WARNER CABLE INC                     0.1446      0.016      9.138      0.000         0.114     0.176\n",
       "stock_TIME WARNER INC.                          0.0757      0.007     11.026      0.000         0.062     0.089\n",
       "stock_TWITTER INC                               0.1978      0.019     10.476      0.000         0.161     0.235\n",
       "stock_UNION PACIFIC CORPORATION                 0.1502      0.013     11.655      0.000         0.125     0.175\n",
       "stock_UNITED CONTINENTAL HOLDINGS IN            0.1245      0.016      7.991      0.000         0.094     0.155\n",
       "stock_UNITED PARCEL SERVICE INC.CL B            0.1248      0.015      8.288      0.000         0.095     0.154\n",
       "stock_UNITED TECHNOLOGIES CORP                  0.1288      0.013      9.937      0.000         0.103     0.154\n",
       "stock_UNITEDHEALTH GROUP INC                    0.1402      0.013     10.559      0.000         0.114     0.166\n",
       "stock_VALERO ENERGY CORP (NEW)                  0.1296      0.013      9.686      0.000         0.103     0.156\n",
       "stock_VERIZON COMMUNICATIONS                    0.1198      0.016      7.496      0.000         0.088     0.151\n",
       "stock_VISA INC                                  0.2206      0.017     12.867      0.000         0.187     0.254\n",
       "stock_WALGREENS BOOTS ALLIANCE INC              0.1369      0.013     10.559      0.000         0.111     0.162\n",
       "stock_WALMART INC                               0.1522      0.021      7.225      0.000         0.111     0.194\n",
       "stock_WALT DISNEY CO                            0.1309      0.010     13.023      0.000         0.111     0.151\n",
       "stock_WELLS FARGO & CO(NEW)                     0.2969      0.043      6.946      0.000         0.213     0.381\n",
       "stock_WILLIAMS COMPANIES                        0.1325      0.013     10.104      0.000         0.107     0.158\n",
       "stock_WYNN RESORTS LTD                          0.1418      0.016      8.999      0.000         0.111     0.173\n",
       "stock_YELP INC                                  0.1947      0.019     10.257      0.000         0.157     0.232\n",
       "==============================================================================\n",
       "Omnibus:                        0.654   Durbin-Watson:                   1.503\n",
       "Prob(Omnibus):                  0.721   Jarque-Bera (JB):                0.669\n",
       "Skew:                          -0.007   Prob(JB):                        0.716\n",
       "Kurtosis:                       2.986   Cond. No.                     6.39e+16\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 6.39e+16. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target = 'Returns10D'\n",
    "model_data = pd.concat([y[[target]], X], axis=1).dropna()\n",
    "model_data = model_data[model_data[target].between(model_data[target].quantile(.025), \n",
    "                                                   model_data[target].quantile(.975))]\n",
    "\n",
    "model = OLS(endog=model_data[target], exog=model_data.drop(target, axis=1))\n",
    "trained_model = model.fit()\n",
    "trained_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Returns20D</td>    <th>  R-squared:         </th>  <td>   0.072</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.068</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   19.10</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 09 Sep 2018</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:18:04</td>     <th>  Log-Likelihood:    </th>  <td>  62705.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 42460</td>      <th>  AIC:               </th> <td>-1.251e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 42288</td>      <th>  BIC:               </th> <td>-1.236e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>   171</td>      <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                       <td></td>                          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>DividendYield</th>                               <td> 1.193e-05</td> <td> 2.23e-06</td> <td>    5.351</td> <td> 0.000</td> <td> 7.56e-06  1.63e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>EBITDAYield</th>                                 <td>   -0.0001</td> <td> 4.51e-05</td> <td>   -3.287</td> <td> 0.001</td> <td>   -0.000 -5.98e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>EVToEBITDA</th>                                  <td>    0.0003</td> <td> 4.07e-05</td> <td>    7.808</td> <td> 0.000</td> <td>    0.000     0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>EVToFCF</th>                                     <td> -3.67e-06</td> <td> 4.99e-05</td> <td>   -0.074</td> <td> 0.941</td> <td>   -0.000  9.42e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PriceToBook</th>                                 <td>  1.86e-05</td> <td> 4.73e-05</td> <td>    0.393</td> <td> 0.694</td> <td>-7.41e-05     0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PriceToDilutedEarningsTTM</th>                   <td>-5.406e-05</td> <td> 2.12e-05</td> <td>   -2.549</td> <td> 0.011</td> <td>-9.56e-05 -1.25e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PriceToEarningsTTM</th>                          <td> -8.22e-07</td> <td> 9.41e-07</td> <td>   -0.873</td> <td> 0.382</td> <td>-2.67e-06  1.02e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PriceToFCF</th>                                  <td> 2.678e-06</td> <td> 4.12e-05</td> <td>    0.065</td> <td> 0.948</td> <td> -7.8e-05  8.33e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PriceToOperatingCashflow</th>                    <td>   -0.0001</td> <td> 2.91e-05</td> <td>   -4.551</td> <td> 0.000</td> <td>   -0.000 -7.54e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PriceToSalesTTM</th>                             <td>-4.302e-05</td> <td>  2.5e-06</td> <td>  -17.222</td> <td> 0.000</td> <td>-4.79e-05 -3.81e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Directional Movement Index</th>                  <td>-3.389e-05</td> <td> 9.51e-06</td> <td>   -3.566</td> <td> 0.000</td> <td>-5.25e-05 -1.53e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Money Flow Index</th>                            <td> 2.884e-05</td> <td> 1.23e-05</td> <td>    2.336</td> <td> 0.019</td> <td> 4.64e-06   5.3e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Percent Above Low</th>                           <td> -7.04e-05</td> <td> 2.47e-05</td> <td>   -2.855</td> <td> 0.004</td> <td>   -0.000 -2.21e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Percent Below High</th>                          <td> 4.825e-05</td> <td> 1.89e-05</td> <td>    2.559</td> <td> 0.010</td> <td> 1.13e-05  8.52e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Price Oscillator</th>                            <td>-5.428e-06</td> <td> 1.33e-05</td> <td>   -0.407</td> <td> 0.684</td> <td>-3.15e-05  2.07e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Trendline</th>                                   <td> 4.502e-05</td> <td> 2.04e-05</td> <td>    2.204</td> <td> 0.028</td> <td> 4.98e-06  8.51e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AssetToEquityRatio</th>                          <td>-8.615e-06</td> <td> 1.86e-06</td> <td>   -4.637</td> <td> 0.000</td> <td>-1.23e-05 -4.97e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AssetTurnover</th>                               <td>   -0.0002</td> <td> 8.11e-05</td> <td>   -2.720</td> <td> 0.007</td> <td>   -0.000 -6.16e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CurrentRatio</th>                                <td>  1.14e-05</td> <td>  2.4e-06</td> <td>    4.756</td> <td> 0.000</td> <td>  6.7e-06  1.61e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>DebtToAssetRatio</th>                            <td> 2.131e-06</td> <td>  1.5e-06</td> <td>    1.421</td> <td> 0.155</td> <td>-8.08e-07  5.07e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>DebtToEquityRatio</th>                           <td> 4.261e-06</td> <td> 1.74e-06</td> <td>    2.445</td> <td> 0.014</td> <td> 8.45e-07  7.68e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MertonsDD</th>                                   <td>   -0.0013</td> <td>    0.000</td> <td>   -6.145</td> <td> 0.000</td> <td>   -0.002    -0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WorkingCapitalToAssets</th>                      <td>  5.08e-06</td> <td> 2.89e-06</td> <td>    1.759</td> <td> 0.079</td> <td> -5.8e-07  1.07e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WorkingCapitalToSales</th>                       <td>   -0.0004</td> <td> 9.31e-05</td> <td>   -4.593</td> <td> 0.000</td> <td>   -0.001    -0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dividend Growth</th>                             <td>-3.037e-06</td> <td> 9.15e-07</td> <td>   -3.319</td> <td> 0.001</td> <td>-4.83e-06 -1.24e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>EPS</th>                                         <td> 2.228e-06</td> <td> 1.41e-06</td> <td>    1.583</td> <td> 0.113</td> <td> -5.3e-07  4.99e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Net Debt</th>                                    <td>-2.368e-13</td> <td> 5.14e-14</td> <td>   -4.609</td> <td> 0.000</td> <td>-3.37e-13 -1.36e-13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sales</th>                                       <td>-2.182e-13</td> <td> 5.03e-14</td> <td>   -4.341</td> <td> 0.000</td> <td>-3.17e-13  -1.2e-13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Total Assets</th>                                <td>-2.513e-13</td> <td> 3.73e-14</td> <td>   -6.741</td> <td> 0.000</td> <td>-3.24e-13 -1.78e-13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>EPS Growth 3M</th>                               <td>-1.786e-05</td> <td> 2.15e-06</td> <td>   -8.320</td> <td> 0.000</td> <td>-2.21e-05 -1.37e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>EPS Growth 12M</th>                              <td> 2.263e-06</td> <td> 2.15e-06</td> <td>    1.053</td> <td> 0.292</td> <td>-1.95e-06  6.47e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Net Debt Growth 3M</th>                          <td> -4.19e-06</td> <td> 2.73e-06</td> <td>   -1.533</td> <td> 0.125</td> <td>-9.55e-06  1.17e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Net Debt Growth 12M</th>                         <td> 1.181e-06</td> <td> 2.76e-06</td> <td>    0.428</td> <td> 0.669</td> <td>-4.23e-06  6.59e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sales Growth 3M</th>                             <td>-7.312e-07</td> <td> 2.88e-06</td> <td>   -0.254</td> <td> 0.800</td> <td>-6.38e-06  4.92e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sales Growth 12M</th>                            <td>-1.036e-05</td> <td> 2.94e-06</td> <td>   -3.525</td> <td> 0.000</td> <td>-1.61e-05  -4.6e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Total Assets Growth 3M</th>                      <td> 7.478e-06</td> <td> 3.24e-06</td> <td>    2.310</td> <td> 0.021</td> <td> 1.13e-06  1.38e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Total Assets Growth 12M</th>                     <td>-3.848e-06</td> <td> 3.44e-06</td> <td>   -1.120</td> <td> 0.263</td> <td>-1.06e-05  2.89e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CFO To Assets</th>                               <td> 9.296e-05</td> <td> 3.95e-05</td> <td>    2.352</td> <td> 0.019</td> <td> 1.55e-05     0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Capex To Assets</th>                             <td>   -0.0002</td> <td> 8.77e-05</td> <td>   -1.768</td> <td> 0.077</td> <td>   -0.000  1.68e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Capex To FCF</th>                                <td>    0.0002</td> <td> 4.86e-05</td> <td>    3.199</td> <td> 0.001</td> <td> 6.03e-05     0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Capex To Sales</th>                              <td>    0.0003</td> <td> 9.32e-05</td> <td>    2.992</td> <td> 0.003</td> <td> 9.62e-05     0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>EBIT To Assets</th>                              <td> 4.836e-06</td> <td>    4e-05</td> <td>    0.121</td> <td> 0.904</td> <td>-7.35e-05  8.32e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Retained Earnings To Assets</th>                 <td>   -0.0010</td> <td> 8.46e-05</td> <td>  -12.222</td> <td> 0.000</td> <td>   -0.001    -0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Downside Risk</th>                               <td>-2.381e-05</td> <td> 2.45e-05</td> <td>   -0.972</td> <td> 0.331</td> <td>-7.18e-05  2.42e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Index Beta</th>                                  <td>-4.429e-06</td> <td> 5.04e-07</td> <td>   -8.793</td> <td> 0.000</td> <td>-5.42e-06 -3.44e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Log Market Cap</th>                              <td>    0.0001</td> <td> 8.62e-05</td> <td>    1.329</td> <td> 0.184</td> <td>-5.44e-05     0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Volatility 3M</th>                               <td>   -0.0002</td> <td> 2.08e-05</td> <td>   -7.260</td> <td> 0.000</td> <td>   -0.000    -0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_3D SYSTEMS CORP</th>                       <td>    0.3173</td> <td>    0.020</td> <td>   15.578</td> <td> 0.000</td> <td>    0.277     0.357</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_3M COMPANY</th>                            <td>    0.3182</td> <td>    0.018</td> <td>   17.973</td> <td> 0.000</td> <td>    0.284     0.353</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_ABBOTT LABORATORIES</th>                   <td>    0.2168</td> <td>    0.014</td> <td>   14.970</td> <td> 0.000</td> <td>    0.188     0.245</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_ABBVIE INC</th>                            <td>    0.3427</td> <td>    0.026</td> <td>   13.306</td> <td> 0.000</td> <td>    0.292     0.393</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_ALLERGAN INC</th>                          <td>    0.3183</td> <td>    0.014</td> <td>   23.540</td> <td> 0.000</td> <td>    0.292     0.345</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_ALLERGAN PLC</th>                          <td>    0.3267</td> <td>    0.019</td> <td>   17.361</td> <td> 0.000</td> <td>    0.290     0.364</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_ALTABA INC</th>                            <td>    0.3407</td> <td>    0.021</td> <td>   16.280</td> <td> 0.000</td> <td>    0.300     0.382</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_ALTRIA GROUP INC.</th>                     <td>    0.3366</td> <td>    0.018</td> <td>   18.421</td> <td> 0.000</td> <td>    0.301     0.372</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_AMAZON.COM INC</th>                        <td>    0.2840</td> <td>    0.021</td> <td>   13.457</td> <td> 0.000</td> <td>    0.243     0.325</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_AMERICAN AIRLINES GROUP INC</th>           <td>    0.2416</td> <td>    0.024</td> <td>   10.047</td> <td> 0.000</td> <td>    0.194     0.289</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_AMERICAN EXPRESS COMPANY</th>              <td>    0.1996</td> <td>    0.011</td> <td>   17.471</td> <td> 0.000</td> <td>    0.177     0.222</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_AMERICAN INTL GROUP INC</th>               <td>    0.2697</td> <td>    0.020</td> <td>   13.737</td> <td> 0.000</td> <td>    0.231     0.308</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_AMGEN INC</th>                             <td>    0.2179</td> <td>    0.012</td> <td>   17.559</td> <td> 0.000</td> <td>    0.194     0.242</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_ANADARKO PETROLEUM CORP</th>               <td>    0.2158</td> <td>    0.011</td> <td>   19.604</td> <td> 0.000</td> <td>    0.194     0.237</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_APACHE CORP</th>                           <td>    0.1494</td> <td>    0.056</td> <td>    2.651</td> <td> 0.008</td> <td>    0.039     0.260</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_APPLE INC</th>                             <td>    0.3383</td> <td>    0.018</td> <td>   19.158</td> <td> 0.000</td> <td>    0.304     0.373</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_APPLIED MATERIALS INC</th>                 <td>    0.2390</td> <td>    0.014</td> <td>   17.579</td> <td> 0.000</td> <td>    0.212     0.266</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_ARCONIC INC</th>                           <td>    0.1068</td> <td>    0.010</td> <td>   10.685</td> <td> 0.000</td> <td>    0.087     0.126</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_AT&T INC. COM</th>                         <td>    0.2514</td> <td>    0.020</td> <td>   12.594</td> <td> 0.000</td> <td>    0.212     0.291</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_Alphabet Inc. Cl A</th>                    <td>    0.4051</td> <td>    0.025</td> <td>   16.157</td> <td> 0.000</td> <td>    0.356     0.454</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_BAKER HUGHES INC</th>                      <td>    0.2139</td> <td>    0.012</td> <td>   17.586</td> <td> 0.000</td> <td>    0.190     0.238</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_BANK OF AMERICA CORP</th>                  <td>    0.7417</td> <td>    0.076</td> <td>    9.797</td> <td> 0.000</td> <td>    0.593     0.890</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_BERKSHIRE HATHAWAY INC CL-B</th>           <td>    0.3567</td> <td>    0.026</td> <td>   13.681</td> <td> 0.000</td> <td>    0.306     0.408</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_BIOGEN INC</th>                            <td>    0.3593</td> <td>    0.017</td> <td>   21.207</td> <td> 0.000</td> <td>    0.326     0.392</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_BOEING CO</th>                             <td>    0.2107</td> <td>    0.013</td> <td>   15.879</td> <td> 0.000</td> <td>    0.185     0.237</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_BOOKING HOLDINGS INC</th>                  <td>    0.3602</td> <td>    0.022</td> <td>   16.449</td> <td> 0.000</td> <td>    0.317     0.403</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_BRISTOL MYERS SQUIBB COMPANY</th>          <td>    0.3115</td> <td>    0.015</td> <td>   20.543</td> <td> 0.000</td> <td>    0.282     0.341</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_BROADCOM CORP</th>                         <td>    0.2813</td> <td>    0.020</td> <td>   14.099</td> <td> 0.000</td> <td>    0.242     0.320</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_BROADCOM INC</th>                          <td>    0.3808</td> <td>    0.026</td> <td>   14.895</td> <td> 0.000</td> <td>    0.331     0.431</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_CATERPILLAR INC</th>                       <td>    0.1730</td> <td>    0.013</td> <td>   13.255</td> <td> 0.000</td> <td>    0.147     0.199</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_CELGENE CORP</th>                          <td>    0.2960</td> <td>    0.015</td> <td>   20.285</td> <td> 0.000</td> <td>    0.267     0.325</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_CHESAPEAKE ENERGY CORP</th>                <td>-4.999e-14</td> <td> 8.77e-14</td> <td>   -0.570</td> <td> 0.568</td> <td>-2.22e-13  1.22e-13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_CHEVRON CORPORATION</th>                   <td>    0.3485</td> <td>    0.027</td> <td>   13.048</td> <td> 0.000</td> <td>    0.296     0.401</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_CISCO SYSTEMS INC</th>                     <td>    0.2298</td> <td>    0.013</td> <td>   17.569</td> <td> 0.000</td> <td>    0.204     0.255</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_CITIGROUP</th>                             <td>    0.6481</td> <td>    0.065</td> <td>    9.957</td> <td> 0.000</td> <td>    0.521     0.776</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_COCA-COLA CO</th>                          <td>    0.2999</td> <td>    0.018</td> <td>   16.645</td> <td> 0.000</td> <td>    0.265     0.335</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_COMCAST CORP</th>                          <td>    0.2309</td> <td>    0.013</td> <td>   17.938</td> <td> 0.000</td> <td>    0.206     0.256</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_CONOCOPHILLIPS</th>                        <td>    0.2784</td> <td>    0.022</td> <td>   12.429</td> <td> 0.000</td> <td>    0.235     0.322</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_COVIDIEN PLC</th>                          <td>    0.3792</td> <td>    0.024</td> <td>   15.807</td> <td> 0.000</td> <td>    0.332     0.426</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_CVS HEALTH CORP</th>                       <td>    0.2568</td> <td>    0.017</td> <td>   15.384</td> <td> 0.000</td> <td>    0.224     0.289</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_DEERE & CO</th>                            <td>    0.1513</td> <td>    0.014</td> <td>   10.763</td> <td> 0.000</td> <td>    0.124     0.179</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_DELTA AIR LINES INC</th>                   <td>    0.2806</td> <td>    0.022</td> <td>   12.572</td> <td> 0.000</td> <td>    0.237     0.324</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_DIRECTV</th>                               <td>    0.2139</td> <td>    0.022</td> <td>    9.864</td> <td> 0.000</td> <td>    0.171     0.256</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_DOLLAR GENERAL CORP</th>                   <td>    0.2719</td> <td>    0.023</td> <td>   11.971</td> <td> 0.000</td> <td>    0.227     0.316</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_DOW CHEMICAL CO</th>                       <td>    0.1912</td> <td>    0.013</td> <td>   14.832</td> <td> 0.000</td> <td>    0.166     0.216</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_E.I. Du Pont De Nemours A</th>             <td>    0.1633</td> <td>    0.013</td> <td>   12.300</td> <td> 0.000</td> <td>    0.137     0.189</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_EBAY INC</th>                              <td>    0.3558</td> <td>    0.023</td> <td>   15.256</td> <td> 0.000</td> <td>    0.310     0.402</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_EMC CORPORATION</th>                       <td>    0.2526</td> <td>    0.013</td> <td>   19.131</td> <td> 0.000</td> <td>    0.227     0.278</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_EOG RESOURCES INC</th>                     <td>    0.2644</td> <td>    0.013</td> <td>   20.854</td> <td> 0.000</td> <td>    0.240     0.289</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_EXPRESS SCRIPTS HOLDING CO</th>            <td>    0.1640</td> <td>    0.014</td> <td>   11.874</td> <td> 0.000</td> <td>    0.137     0.191</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_EXXON MOBIL CORPORATION</th>               <td>    0.4155</td> <td>    0.031</td> <td>   13.352</td> <td> 0.000</td> <td>    0.355     0.477</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_FACEBOOK INC</th>                          <td>    0.3934</td> <td>    0.026</td> <td>   14.853</td> <td> 0.000</td> <td>    0.341     0.445</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_FEDEX CORPORATION</th>                     <td>    0.2296</td> <td>    0.014</td> <td>   16.546</td> <td> 0.000</td> <td>    0.202     0.257</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_FIRST SOLAR INC</th>                       <td>    0.3130</td> <td>    0.025</td> <td>   12.617</td> <td> 0.000</td> <td>    0.264     0.362</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_FORD MOTOR CO(NEW)</th>                    <td>    0.1766</td> <td>    0.017</td> <td>   10.680</td> <td> 0.000</td> <td>    0.144     0.209</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_FREEPORT-MCMORAN INC</th>                  <td>    0.1663</td> <td>    0.018</td> <td>    9.357</td> <td> 0.000</td> <td>    0.131     0.201</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_GENERAL ELECTRIC CO</th>                   <td>    0.3810</td> <td>    0.026</td> <td>   14.621</td> <td> 0.000</td> <td>    0.330     0.432</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_GENERAL MOTORS CO</th>                     <td>    0.2661</td> <td>    0.026</td> <td>   10.406</td> <td> 0.000</td> <td>    0.216     0.316</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_GILEAD SCIENCES INC</th>                   <td>    0.3193</td> <td>    0.016</td> <td>   20.030</td> <td> 0.000</td> <td>    0.288     0.351</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_GOLDMAN SACHS GROUP INC</th>               <td>    0.5289</td> <td>    0.035</td> <td>   14.945</td> <td> 0.000</td> <td>    0.460     0.598</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_GOPRO INC</th>                             <td>    0.3186</td> <td>    0.026</td> <td>   12.297</td> <td> 0.000</td> <td>    0.268     0.369</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_HALLIBURTON CO (HOLDING CO)</th>           <td>    0.2384</td> <td>    0.014</td> <td>   16.933</td> <td> 0.000</td> <td>    0.211     0.266</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_HOME DEPOT INC</th>                        <td>    0.2761</td> <td>    0.016</td> <td>   17.535</td> <td> 0.000</td> <td>    0.245     0.307</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_HP INC</th>                                <td>    0.2021</td> <td>    0.015</td> <td>   13.749</td> <td> 0.000</td> <td>    0.173     0.231</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_INTEL CORP</th>                            <td>    0.2633</td> <td>    0.016</td> <td>   16.526</td> <td> 0.000</td> <td>    0.232     0.294</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_INTL BUSINESS MACHINES CORP</th>           <td>    0.2830</td> <td>    0.018</td> <td>   15.965</td> <td> 0.000</td> <td>    0.248     0.318</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_JOHNSON AND JOHNSON</th>                   <td>    0.3200</td> <td>    0.018</td> <td>   17.592</td> <td> 0.000</td> <td>    0.284     0.356</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_JPMORGAN CHASE & CO COM STK</th>           <td>    0.8913</td> <td>    0.092</td> <td>    9.698</td> <td> 0.000</td> <td>    0.711     1.071</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_KEURIG GREEN MOUNTAIN INC</th>             <td>    0.3387</td> <td>    0.019</td> <td>   18.109</td> <td> 0.000</td> <td>    0.302     0.375</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_KINDER MORGAN INC</th>                     <td>    0.2413</td> <td>    0.025</td> <td>    9.811</td> <td> 0.000</td> <td>    0.193     0.289</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_LAS VEGAS SANDS CORP</th>                  <td>    0.2950</td> <td>    0.024</td> <td>   12.543</td> <td> 0.000</td> <td>    0.249     0.341</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_LILLY ELI & CO</th>                        <td>    0.2792</td> <td>    0.016</td> <td>   16.965</td> <td> 0.000</td> <td>    0.247     0.311</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_LINKEDIN CORP</th>                         <td>    0.3443</td> <td>    0.026</td> <td>   13.316</td> <td> 0.000</td> <td>    0.294     0.395</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_LOWES COMPANIES INC</th>                   <td>    0.2297</td> <td>    0.014</td> <td>   16.545</td> <td> 0.000</td> <td>    0.202     0.257</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_LYONDELLBASELL INDUSTRIES NV</th>          <td>    0.2773</td> <td>    0.024</td> <td>   11.680</td> <td> 0.000</td> <td>    0.231     0.324</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MARATHON PETROLEUM CORP</th>               <td>    0.2467</td> <td>    0.026</td> <td>    9.411</td> <td> 0.000</td> <td>    0.195     0.298</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MASTERCARD INCORPORATED</th>               <td>    0.4555</td> <td>    0.025</td> <td>   17.920</td> <td> 0.000</td> <td>    0.406     0.505</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MCDONALDS CORP</th>                        <td>    0.2920</td> <td>    0.018</td> <td>   16.033</td> <td> 0.000</td> <td>    0.256     0.328</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MEDTRONIC PLC</th>                         <td>    0.3192</td> <td>    0.016</td> <td>   19.660</td> <td> 0.000</td> <td>    0.287     0.351</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MERCK & CO INC</th>                        <td>    0.3003</td> <td>    0.017</td> <td>   17.338</td> <td> 0.000</td> <td>    0.266     0.334</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_METLIFE  INC</th>                          <td>    0.3781</td> <td>    0.037</td> <td>   10.338</td> <td> 0.000</td> <td>    0.306     0.450</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MICHAEL KORS HOLDINGS LTD</th>             <td>    0.3756</td> <td>    0.027</td> <td>   13.904</td> <td> 0.000</td> <td>    0.323     0.429</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MICRON TECHNOLOGY INC</th>                 <td>    0.2171</td> <td>    0.014</td> <td>   15.211</td> <td> 0.000</td> <td>    0.189     0.245</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MICROSOFT CORP</th>                        <td>    0.3047</td> <td>    0.018</td> <td>   17.242</td> <td> 0.000</td> <td>    0.270     0.339</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MONDELEZ INTERNATIONAL INC</th>            <td>    0.2891</td> <td>    0.021</td> <td>   13.662</td> <td> 0.000</td> <td>    0.248     0.331</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MONSANTO COMPANY</th>                      <td>    0.3312</td> <td>    0.022</td> <td>   15.138</td> <td> 0.000</td> <td>    0.288     0.374</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MORGAN STANLEY</th>                        <td>    0.4563</td> <td>    0.033</td> <td>   13.722</td> <td> 0.000</td> <td>    0.391     0.521</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_MYLAN NV</th>                              <td>    0.2558</td> <td>    0.016</td> <td>   16.113</td> <td> 0.000</td> <td>    0.225     0.287</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_NATIONAL OILWELL VARCO  INC.</th>          <td>    0.2658</td> <td>    0.023</td> <td>   11.681</td> <td> 0.000</td> <td>    0.221     0.310</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_NETFLIX INC</th>                           <td>    0.3875</td> <td>    0.023</td> <td>   16.682</td> <td> 0.000</td> <td>    0.342     0.433</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_NEWMONT MINING CORP (HOLDING COMPANY)</th> <td>    0.1446</td> <td>    0.017</td> <td>    8.514</td> <td> 0.000</td> <td>    0.111     0.178</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_NEWS CP - CL A</th>                        <td>    0.2669</td> <td>    0.018</td> <td>   15.067</td> <td> 0.000</td> <td>    0.232     0.302</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_NIKE INC CL-B</th>                         <td>    0.3016</td> <td>    0.017</td> <td>   18.078</td> <td> 0.000</td> <td>    0.269     0.334</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_OCCIDENTAL PETROLEUM CORP</th>             <td>    0.2756</td> <td>    0.017</td> <td>   15.892</td> <td> 0.000</td> <td>    0.242     0.310</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_ORACLE CORP</th>                           <td>    0.2984</td> <td>    0.017</td> <td>   18.058</td> <td> 0.000</td> <td>    0.266     0.331</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_PANDORA MEDIA INC</th>                     <td>    0.3925</td> <td>    0.028</td> <td>   14.067</td> <td> 0.000</td> <td>    0.338     0.447</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_PENNEY J.C. CO INC (HOLDING COMPANY)</th>  <td>    0.0737</td> <td>    0.014</td> <td>    5.167</td> <td> 0.000</td> <td>    0.046     0.102</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_PEPSICO INC</th>                           <td>    0.2931</td> <td>    0.019</td> <td>   15.449</td> <td> 0.000</td> <td>    0.256     0.330</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_PFIZER INC</th>                            <td>    0.3336</td> <td>    0.019</td> <td>   17.563</td> <td> 0.000</td> <td>    0.296     0.371</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_PHILIP MORRIS INTERNATIONAL INC</th>       <td>    0.3844</td> <td>    0.028</td> <td>   13.959</td> <td> 0.000</td> <td>    0.330     0.438</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_PIONEER NAT RES CO</th>                    <td>    0.3579</td> <td>    0.020</td> <td>   17.747</td> <td> 0.000</td> <td>    0.318     0.397</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_PRECISION CASTPARTS CORP</th>              <td>    0.3008</td> <td>    0.020</td> <td>   14.774</td> <td> 0.000</td> <td>    0.261     0.341</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_PROCTER & GAMBLE CO</th>                   <td>    0.3094</td> <td>    0.020</td> <td>   15.745</td> <td> 0.000</td> <td>    0.271     0.348</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_QUALCOMM INC</th>                          <td>    0.3232</td> <td>    0.019</td> <td>   17.225</td> <td> 0.000</td> <td>    0.286     0.360</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_REGENERON PHARMACEUTICALS INC</th>         <td>    0.3153</td> <td>    0.043</td> <td>    7.267</td> <td> 0.000</td> <td>    0.230     0.400</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_SALESFORCE.COM INC</th>                    <td>    0.3156</td> <td>    0.023</td> <td>   13.571</td> <td> 0.000</td> <td>    0.270     0.361</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_SALIX PHARMACEUTICALS LTD</th>             <td>-5.165e-15</td> <td> 7.41e-15</td> <td>   -0.697</td> <td> 0.486</td> <td>-1.97e-14  9.37e-15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_SANDISK CORP</th>                          <td>    0.3350</td> <td>    0.020</td> <td>   17.064</td> <td> 0.000</td> <td>    0.296     0.373</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_SCHLUMBERGER LTD.</th>                     <td>    0.3011</td> <td>    0.018</td> <td>   16.396</td> <td> 0.000</td> <td>    0.265     0.337</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_SKYWORKS SOLUTIONS INC</th>                <td>    0.3422</td> <td>    0.023</td> <td>   14.843</td> <td> 0.000</td> <td>    0.297     0.387</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_SOLARCITY CORP</th>                        <td>    0.3803</td> <td>    0.027</td> <td>   14.070</td> <td> 0.000</td> <td>    0.327     0.433</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_SOUTHWEST AIRLINES CO</th>                 <td>    0.2513</td> <td>    0.015</td> <td>   16.796</td> <td> 0.000</td> <td>    0.222     0.281</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_STARBUCKS CORPORATION</th>                 <td>    0.3558</td> <td>    0.019</td> <td>   19.139</td> <td> 0.000</td> <td>    0.319     0.392</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_SUNEDISON INC</th>                         <td> 1.713e-16</td> <td> 2.85e-16</td> <td>    0.601</td> <td> 0.548</td> <td>-3.87e-16   7.3e-16</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_TARGET CORPORATION</th>                    <td>    0.2298</td> <td>    0.020</td> <td>   11.314</td> <td> 0.000</td> <td>    0.190     0.270</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_TESLA INC</th>                             <td>    0.3540</td> <td>    0.026</td> <td>   13.789</td> <td> 0.000</td> <td>    0.304     0.404</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_TEXAS INSTRUMENTS INC</th>                 <td>    0.3573</td> <td>    0.021</td> <td>   17.423</td> <td> 0.000</td> <td>    0.317     0.397</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_TIME WARNER CABLE INC</th>                 <td>    0.2637</td> <td>    0.022</td> <td>   11.821</td> <td> 0.000</td> <td>    0.220     0.307</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_TIME WARNER INC.</th>                      <td>    0.1773</td> <td>    0.010</td> <td>   18.052</td> <td> 0.000</td> <td>    0.158     0.197</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_TWITTER INC</th>                           <td>    0.3333</td> <td>    0.027</td> <td>   12.463</td> <td> 0.000</td> <td>    0.281     0.386</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_UNION PACIFIC CORPORATION</th>             <td>    0.3164</td> <td>    0.018</td> <td>   17.283</td> <td> 0.000</td> <td>    0.281     0.352</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_UNITED CONTINENTAL HOLDINGS IN</th>        <td>    0.1975</td> <td>    0.022</td> <td>    8.977</td> <td> 0.000</td> <td>    0.154     0.241</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_UNITED PARCEL SERVICE INC.CL B</th>        <td>    0.2391</td> <td>    0.021</td> <td>   11.199</td> <td> 0.000</td> <td>    0.197     0.281</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_UNITED TECHNOLOGIES CORP</th>              <td>    0.2750</td> <td>    0.018</td> <td>   14.928</td> <td> 0.000</td> <td>    0.239     0.311</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_UNITEDHEALTH GROUP INC</th>                <td>    0.2970</td> <td>    0.019</td> <td>   15.679</td> <td> 0.000</td> <td>    0.260     0.334</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_VALERO ENERGY CORP (NEW)</th>              <td>    0.2539</td> <td>    0.019</td> <td>   13.356</td> <td> 0.000</td> <td>    0.217     0.291</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_VERIZON COMMUNICATIONS</th>                <td>    0.2472</td> <td>    0.023</td> <td>   10.825</td> <td> 0.000</td> <td>    0.202     0.292</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_VISA INC</th>                              <td>    0.4192</td> <td>    0.024</td> <td>   17.309</td> <td> 0.000</td> <td>    0.372     0.467</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_WALGREENS BOOTS ALLIANCE INC</th>          <td>    0.2793</td> <td>    0.018</td> <td>   15.194</td> <td> 0.000</td> <td>    0.243     0.315</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_WALMART INC</th>                           <td>    0.3251</td> <td>    0.031</td> <td>   10.594</td> <td> 0.000</td> <td>    0.265     0.385</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_WALT DISNEY CO</th>                        <td>    0.3131</td> <td>    0.014</td> <td>   21.827</td> <td> 0.000</td> <td>    0.285     0.341</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_WELLS FARGO & CO(NEW)</th>                 <td>    0.7175</td> <td>    0.062</td> <td>   11.586</td> <td> 0.000</td> <td>    0.596     0.839</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_WILLIAMS COMPANIES</th>                    <td>    0.2522</td> <td>    0.019</td> <td>   13.501</td> <td> 0.000</td> <td>    0.216     0.289</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_WYNN RESORTS LTD</th>                      <td>    0.2461</td> <td>    0.022</td> <td>   11.047</td> <td> 0.000</td> <td>    0.202     0.290</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>stock_YELP INC</th>                              <td>    0.3195</td> <td>    0.027</td> <td>   11.882</td> <td> 0.000</td> <td>    0.267     0.372</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>26.157</td> <th>  Durbin-Watson:     </th> <td>   1.605</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  26.187</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.059</td> <th>  Prob(JB):          </th> <td>2.06e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.970</td> <th>  Cond. No.          </th> <td>1.95e+19</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             Returns20D   R-squared:                       0.072\n",
       "Model:                            OLS   Adj. R-squared:                  0.068\n",
       "Method:                 Least Squares   F-statistic:                     19.10\n",
       "Date:                Sun, 09 Sep 2018   Prob (F-statistic):               0.00\n",
       "Time:                        21:18:04   Log-Likelihood:                 62705.\n",
       "No. Observations:               42460   AIC:                        -1.251e+05\n",
       "Df Residuals:                   42288   BIC:                        -1.236e+05\n",
       "Df Model:                         171                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===============================================================================================================\n",
       "                                                  coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
       "---------------------------------------------------------------------------------------------------------------\n",
       "DividendYield                                1.193e-05   2.23e-06      5.351      0.000      7.56e-06  1.63e-05\n",
       "EBITDAYield                                    -0.0001   4.51e-05     -3.287      0.001        -0.000 -5.98e-05\n",
       "EVToEBITDA                                      0.0003   4.07e-05      7.808      0.000         0.000     0.000\n",
       "EVToFCF                                      -3.67e-06   4.99e-05     -0.074      0.941        -0.000  9.42e-05\n",
       "PriceToBook                                   1.86e-05   4.73e-05      0.393      0.694     -7.41e-05     0.000\n",
       "PriceToDilutedEarningsTTM                   -5.406e-05   2.12e-05     -2.549      0.011     -9.56e-05 -1.25e-05\n",
       "PriceToEarningsTTM                           -8.22e-07   9.41e-07     -0.873      0.382     -2.67e-06  1.02e-06\n",
       "PriceToFCF                                   2.678e-06   4.12e-05      0.065      0.948      -7.8e-05  8.33e-05\n",
       "PriceToOperatingCashflow                       -0.0001   2.91e-05     -4.551      0.000        -0.000 -7.54e-05\n",
       "PriceToSalesTTM                             -4.302e-05    2.5e-06    -17.222      0.000     -4.79e-05 -3.81e-05\n",
       "Directional Movement Index                  -3.389e-05   9.51e-06     -3.566      0.000     -5.25e-05 -1.53e-05\n",
       "Money Flow Index                             2.884e-05   1.23e-05      2.336      0.019      4.64e-06   5.3e-05\n",
       "Percent Above Low                            -7.04e-05   2.47e-05     -2.855      0.004        -0.000 -2.21e-05\n",
       "Percent Below High                           4.825e-05   1.89e-05      2.559      0.010      1.13e-05  8.52e-05\n",
       "Price Oscillator                            -5.428e-06   1.33e-05     -0.407      0.684     -3.15e-05  2.07e-05\n",
       "Trendline                                    4.502e-05   2.04e-05      2.204      0.028      4.98e-06  8.51e-05\n",
       "AssetToEquityRatio                          -8.615e-06   1.86e-06     -4.637      0.000     -1.23e-05 -4.97e-06\n",
       "AssetTurnover                                  -0.0002   8.11e-05     -2.720      0.007        -0.000 -6.16e-05\n",
       "CurrentRatio                                  1.14e-05    2.4e-06      4.756      0.000       6.7e-06  1.61e-05\n",
       "DebtToAssetRatio                             2.131e-06    1.5e-06      1.421      0.155     -8.08e-07  5.07e-06\n",
       "DebtToEquityRatio                            4.261e-06   1.74e-06      2.445      0.014      8.45e-07  7.68e-06\n",
       "MertonsDD                                      -0.0013      0.000     -6.145      0.000        -0.002    -0.001\n",
       "WorkingCapitalToAssets                        5.08e-06   2.89e-06      1.759      0.079      -5.8e-07  1.07e-05\n",
       "WorkingCapitalToSales                          -0.0004   9.31e-05     -4.593      0.000        -0.001    -0.000\n",
       "Dividend Growth                             -3.037e-06   9.15e-07     -3.319      0.001     -4.83e-06 -1.24e-06\n",
       "EPS                                          2.228e-06   1.41e-06      1.583      0.113      -5.3e-07  4.99e-06\n",
       "Net Debt                                    -2.368e-13   5.14e-14     -4.609      0.000     -3.37e-13 -1.36e-13\n",
       "Sales                                       -2.182e-13   5.03e-14     -4.341      0.000     -3.17e-13  -1.2e-13\n",
       "Total Assets                                -2.513e-13   3.73e-14     -6.741      0.000     -3.24e-13 -1.78e-13\n",
       "EPS Growth 3M                               -1.786e-05   2.15e-06     -8.320      0.000     -2.21e-05 -1.37e-05\n",
       "EPS Growth 12M                               2.263e-06   2.15e-06      1.053      0.292     -1.95e-06  6.47e-06\n",
       "Net Debt Growth 3M                           -4.19e-06   2.73e-06     -1.533      0.125     -9.55e-06  1.17e-06\n",
       "Net Debt Growth 12M                          1.181e-06   2.76e-06      0.428      0.669     -4.23e-06  6.59e-06\n",
       "Sales Growth 3M                             -7.312e-07   2.88e-06     -0.254      0.800     -6.38e-06  4.92e-06\n",
       "Sales Growth 12M                            -1.036e-05   2.94e-06     -3.525      0.000     -1.61e-05  -4.6e-06\n",
       "Total Assets Growth 3M                       7.478e-06   3.24e-06      2.310      0.021      1.13e-06  1.38e-05\n",
       "Total Assets Growth 12M                     -3.848e-06   3.44e-06     -1.120      0.263     -1.06e-05  2.89e-06\n",
       "CFO To Assets                                9.296e-05   3.95e-05      2.352      0.019      1.55e-05     0.000\n",
       "Capex To Assets                                -0.0002   8.77e-05     -1.768      0.077        -0.000  1.68e-05\n",
       "Capex To FCF                                    0.0002   4.86e-05      3.199      0.001      6.03e-05     0.000\n",
       "Capex To Sales                                  0.0003   9.32e-05      2.992      0.003      9.62e-05     0.000\n",
       "EBIT To Assets                               4.836e-06      4e-05      0.121      0.904     -7.35e-05  8.32e-05\n",
       "Retained Earnings To Assets                    -0.0010   8.46e-05    -12.222      0.000        -0.001    -0.001\n",
       "Downside Risk                               -2.381e-05   2.45e-05     -0.972      0.331     -7.18e-05  2.42e-05\n",
       "Index Beta                                  -4.429e-06   5.04e-07     -8.793      0.000     -5.42e-06 -3.44e-06\n",
       "Log Market Cap                                  0.0001   8.62e-05      1.329      0.184     -5.44e-05     0.000\n",
       "Volatility 3M                                  -0.0002   2.08e-05     -7.260      0.000        -0.000    -0.000\n",
       "stock_3D SYSTEMS CORP                           0.3173      0.020     15.578      0.000         0.277     0.357\n",
       "stock_3M COMPANY                                0.3182      0.018     17.973      0.000         0.284     0.353\n",
       "stock_ABBOTT LABORATORIES                       0.2168      0.014     14.970      0.000         0.188     0.245\n",
       "stock_ABBVIE INC                                0.3427      0.026     13.306      0.000         0.292     0.393\n",
       "stock_ALLERGAN INC                              0.3183      0.014     23.540      0.000         0.292     0.345\n",
       "stock_ALLERGAN PLC                              0.3267      0.019     17.361      0.000         0.290     0.364\n",
       "stock_ALTABA INC                                0.3407      0.021     16.280      0.000         0.300     0.382\n",
       "stock_ALTRIA GROUP INC.                         0.3366      0.018     18.421      0.000         0.301     0.372\n",
       "stock_AMAZON.COM INC                            0.2840      0.021     13.457      0.000         0.243     0.325\n",
       "stock_AMERICAN AIRLINES GROUP INC               0.2416      0.024     10.047      0.000         0.194     0.289\n",
       "stock_AMERICAN EXPRESS COMPANY                  0.1996      0.011     17.471      0.000         0.177     0.222\n",
       "stock_AMERICAN INTL GROUP INC                   0.2697      0.020     13.737      0.000         0.231     0.308\n",
       "stock_AMGEN INC                                 0.2179      0.012     17.559      0.000         0.194     0.242\n",
       "stock_ANADARKO PETROLEUM CORP                   0.2158      0.011     19.604      0.000         0.194     0.237\n",
       "stock_APACHE CORP                               0.1494      0.056      2.651      0.008         0.039     0.260\n",
       "stock_APPLE INC                                 0.3383      0.018     19.158      0.000         0.304     0.373\n",
       "stock_APPLIED MATERIALS INC                     0.2390      0.014     17.579      0.000         0.212     0.266\n",
       "stock_ARCONIC INC                               0.1068      0.010     10.685      0.000         0.087     0.126\n",
       "stock_AT&T INC. COM                             0.2514      0.020     12.594      0.000         0.212     0.291\n",
       "stock_Alphabet Inc. Cl A                        0.4051      0.025     16.157      0.000         0.356     0.454\n",
       "stock_BAKER HUGHES INC                          0.2139      0.012     17.586      0.000         0.190     0.238\n",
       "stock_BANK OF AMERICA CORP                      0.7417      0.076      9.797      0.000         0.593     0.890\n",
       "stock_BERKSHIRE HATHAWAY INC CL-B               0.3567      0.026     13.681      0.000         0.306     0.408\n",
       "stock_BIOGEN INC                                0.3593      0.017     21.207      0.000         0.326     0.392\n",
       "stock_BOEING CO                                 0.2107      0.013     15.879      0.000         0.185     0.237\n",
       "stock_BOOKING HOLDINGS INC                      0.3602      0.022     16.449      0.000         0.317     0.403\n",
       "stock_BRISTOL MYERS SQUIBB COMPANY              0.3115      0.015     20.543      0.000         0.282     0.341\n",
       "stock_BROADCOM CORP                             0.2813      0.020     14.099      0.000         0.242     0.320\n",
       "stock_BROADCOM INC                              0.3808      0.026     14.895      0.000         0.331     0.431\n",
       "stock_CATERPILLAR INC                           0.1730      0.013     13.255      0.000         0.147     0.199\n",
       "stock_CELGENE CORP                              0.2960      0.015     20.285      0.000         0.267     0.325\n",
       "stock_CHESAPEAKE ENERGY CORP                -4.999e-14   8.77e-14     -0.570      0.568     -2.22e-13  1.22e-13\n",
       "stock_CHEVRON CORPORATION                       0.3485      0.027     13.048      0.000         0.296     0.401\n",
       "stock_CISCO SYSTEMS INC                         0.2298      0.013     17.569      0.000         0.204     0.255\n",
       "stock_CITIGROUP                                 0.6481      0.065      9.957      0.000         0.521     0.776\n",
       "stock_COCA-COLA CO                              0.2999      0.018     16.645      0.000         0.265     0.335\n",
       "stock_COMCAST CORP                              0.2309      0.013     17.938      0.000         0.206     0.256\n",
       "stock_CONOCOPHILLIPS                            0.2784      0.022     12.429      0.000         0.235     0.322\n",
       "stock_COVIDIEN PLC                              0.3792      0.024     15.807      0.000         0.332     0.426\n",
       "stock_CVS HEALTH CORP                           0.2568      0.017     15.384      0.000         0.224     0.289\n",
       "stock_DEERE & CO                                0.1513      0.014     10.763      0.000         0.124     0.179\n",
       "stock_DELTA AIR LINES INC                       0.2806      0.022     12.572      0.000         0.237     0.324\n",
       "stock_DIRECTV                                   0.2139      0.022      9.864      0.000         0.171     0.256\n",
       "stock_DOLLAR GENERAL CORP                       0.2719      0.023     11.971      0.000         0.227     0.316\n",
       "stock_DOW CHEMICAL CO                           0.1912      0.013     14.832      0.000         0.166     0.216\n",
       "stock_E.I. Du Pont De Nemours A                 0.1633      0.013     12.300      0.000         0.137     0.189\n",
       "stock_EBAY INC                                  0.3558      0.023     15.256      0.000         0.310     0.402\n",
       "stock_EMC CORPORATION                           0.2526      0.013     19.131      0.000         0.227     0.278\n",
       "stock_EOG RESOURCES INC                         0.2644      0.013     20.854      0.000         0.240     0.289\n",
       "stock_EXPRESS SCRIPTS HOLDING CO                0.1640      0.014     11.874      0.000         0.137     0.191\n",
       "stock_EXXON MOBIL CORPORATION                   0.4155      0.031     13.352      0.000         0.355     0.477\n",
       "stock_FACEBOOK INC                              0.3934      0.026     14.853      0.000         0.341     0.445\n",
       "stock_FEDEX CORPORATION                         0.2296      0.014     16.546      0.000         0.202     0.257\n",
       "stock_FIRST SOLAR INC                           0.3130      0.025     12.617      0.000         0.264     0.362\n",
       "stock_FORD MOTOR CO(NEW)                        0.1766      0.017     10.680      0.000         0.144     0.209\n",
       "stock_FREEPORT-MCMORAN INC                      0.1663      0.018      9.357      0.000         0.131     0.201\n",
       "stock_GENERAL ELECTRIC CO                       0.3810      0.026     14.621      0.000         0.330     0.432\n",
       "stock_GENERAL MOTORS CO                         0.2661      0.026     10.406      0.000         0.216     0.316\n",
       "stock_GILEAD SCIENCES INC                       0.3193      0.016     20.030      0.000         0.288     0.351\n",
       "stock_GOLDMAN SACHS GROUP INC                   0.5289      0.035     14.945      0.000         0.460     0.598\n",
       "stock_GOPRO INC                                 0.3186      0.026     12.297      0.000         0.268     0.369\n",
       "stock_HALLIBURTON CO (HOLDING CO)               0.2384      0.014     16.933      0.000         0.211     0.266\n",
       "stock_HOME DEPOT INC                            0.2761      0.016     17.535      0.000         0.245     0.307\n",
       "stock_HP INC                                    0.2021      0.015     13.749      0.000         0.173     0.231\n",
       "stock_INTEL CORP                                0.2633      0.016     16.526      0.000         0.232     0.294\n",
       "stock_INTL BUSINESS MACHINES CORP               0.2830      0.018     15.965      0.000         0.248     0.318\n",
       "stock_JOHNSON AND JOHNSON                       0.3200      0.018     17.592      0.000         0.284     0.356\n",
       "stock_JPMORGAN CHASE & CO COM STK               0.8913      0.092      9.698      0.000         0.711     1.071\n",
       "stock_KEURIG GREEN MOUNTAIN INC                 0.3387      0.019     18.109      0.000         0.302     0.375\n",
       "stock_KINDER MORGAN INC                         0.2413      0.025      9.811      0.000         0.193     0.289\n",
       "stock_LAS VEGAS SANDS CORP                      0.2950      0.024     12.543      0.000         0.249     0.341\n",
       "stock_LILLY ELI & CO                            0.2792      0.016     16.965      0.000         0.247     0.311\n",
       "stock_LINKEDIN CORP                             0.3443      0.026     13.316      0.000         0.294     0.395\n",
       "stock_LOWES COMPANIES INC                       0.2297      0.014     16.545      0.000         0.202     0.257\n",
       "stock_LYONDELLBASELL INDUSTRIES NV              0.2773      0.024     11.680      0.000         0.231     0.324\n",
       "stock_MARATHON PETROLEUM CORP                   0.2467      0.026      9.411      0.000         0.195     0.298\n",
       "stock_MASTERCARD INCORPORATED                   0.4555      0.025     17.920      0.000         0.406     0.505\n",
       "stock_MCDONALDS CORP                            0.2920      0.018     16.033      0.000         0.256     0.328\n",
       "stock_MEDTRONIC PLC                             0.3192      0.016     19.660      0.000         0.287     0.351\n",
       "stock_MERCK & CO INC                            0.3003      0.017     17.338      0.000         0.266     0.334\n",
       "stock_METLIFE  INC                              0.3781      0.037     10.338      0.000         0.306     0.450\n",
       "stock_MICHAEL KORS HOLDINGS LTD                 0.3756      0.027     13.904      0.000         0.323     0.429\n",
       "stock_MICRON TECHNOLOGY INC                     0.2171      0.014     15.211      0.000         0.189     0.245\n",
       "stock_MICROSOFT CORP                            0.3047      0.018     17.242      0.000         0.270     0.339\n",
       "stock_MONDELEZ INTERNATIONAL INC                0.2891      0.021     13.662      0.000         0.248     0.331\n",
       "stock_MONSANTO COMPANY                          0.3312      0.022     15.138      0.000         0.288     0.374\n",
       "stock_MORGAN STANLEY                            0.4563      0.033     13.722      0.000         0.391     0.521\n",
       "stock_MYLAN NV                                  0.2558      0.016     16.113      0.000         0.225     0.287\n",
       "stock_NATIONAL OILWELL VARCO  INC.              0.2658      0.023     11.681      0.000         0.221     0.310\n",
       "stock_NETFLIX INC                               0.3875      0.023     16.682      0.000         0.342     0.433\n",
       "stock_NEWMONT MINING CORP (HOLDING COMPANY)     0.1446      0.017      8.514      0.000         0.111     0.178\n",
       "stock_NEWS CP - CL A                            0.2669      0.018     15.067      0.000         0.232     0.302\n",
       "stock_NIKE INC CL-B                             0.3016      0.017     18.078      0.000         0.269     0.334\n",
       "stock_OCCIDENTAL PETROLEUM CORP                 0.2756      0.017     15.892      0.000         0.242     0.310\n",
       "stock_ORACLE CORP                               0.2984      0.017     18.058      0.000         0.266     0.331\n",
       "stock_PANDORA MEDIA INC                         0.3925      0.028     14.067      0.000         0.338     0.447\n",
       "stock_PENNEY J.C. CO INC (HOLDING COMPANY)      0.0737      0.014      5.167      0.000         0.046     0.102\n",
       "stock_PEPSICO INC                               0.2931      0.019     15.449      0.000         0.256     0.330\n",
       "stock_PFIZER INC                                0.3336      0.019     17.563      0.000         0.296     0.371\n",
       "stock_PHILIP MORRIS INTERNATIONAL INC           0.3844      0.028     13.959      0.000         0.330     0.438\n",
       "stock_PIONEER NAT RES CO                        0.3579      0.020     17.747      0.000         0.318     0.397\n",
       "stock_PRECISION CASTPARTS CORP                  0.3008      0.020     14.774      0.000         0.261     0.341\n",
       "stock_PROCTER & GAMBLE CO                       0.3094      0.020     15.745      0.000         0.271     0.348\n",
       "stock_QUALCOMM INC                              0.3232      0.019     17.225      0.000         0.286     0.360\n",
       "stock_REGENERON PHARMACEUTICALS INC             0.3153      0.043      7.267      0.000         0.230     0.400\n",
       "stock_SALESFORCE.COM INC                        0.3156      0.023     13.571      0.000         0.270     0.361\n",
       "stock_SALIX PHARMACEUTICALS LTD             -5.165e-15   7.41e-15     -0.697      0.486     -1.97e-14  9.37e-15\n",
       "stock_SANDISK CORP                              0.3350      0.020     17.064      0.000         0.296     0.373\n",
       "stock_SCHLUMBERGER LTD.                         0.3011      0.018     16.396      0.000         0.265     0.337\n",
       "stock_SKYWORKS SOLUTIONS INC                    0.3422      0.023     14.843      0.000         0.297     0.387\n",
       "stock_SOLARCITY CORP                            0.3803      0.027     14.070      0.000         0.327     0.433\n",
       "stock_SOUTHWEST AIRLINES CO                     0.2513      0.015     16.796      0.000         0.222     0.281\n",
       "stock_STARBUCKS CORPORATION                     0.3558      0.019     19.139      0.000         0.319     0.392\n",
       "stock_SUNEDISON INC                          1.713e-16   2.85e-16      0.601      0.548     -3.87e-16   7.3e-16\n",
       "stock_TARGET CORPORATION                        0.2298      0.020     11.314      0.000         0.190     0.270\n",
       "stock_TESLA INC                                 0.3540      0.026     13.789      0.000         0.304     0.404\n",
       "stock_TEXAS INSTRUMENTS INC                     0.3573      0.021     17.423      0.000         0.317     0.397\n",
       "stock_TIME WARNER CABLE INC                     0.2637      0.022     11.821      0.000         0.220     0.307\n",
       "stock_TIME WARNER INC.                          0.1773      0.010     18.052      0.000         0.158     0.197\n",
       "stock_TWITTER INC                               0.3333      0.027     12.463      0.000         0.281     0.386\n",
       "stock_UNION PACIFIC CORPORATION                 0.3164      0.018     17.283      0.000         0.281     0.352\n",
       "stock_UNITED CONTINENTAL HOLDINGS IN            0.1975      0.022      8.977      0.000         0.154     0.241\n",
       "stock_UNITED PARCEL SERVICE INC.CL B            0.2391      0.021     11.199      0.000         0.197     0.281\n",
       "stock_UNITED TECHNOLOGIES CORP                  0.2750      0.018     14.928      0.000         0.239     0.311\n",
       "stock_UNITEDHEALTH GROUP INC                    0.2970      0.019     15.679      0.000         0.260     0.334\n",
       "stock_VALERO ENERGY CORP (NEW)                  0.2539      0.019     13.356      0.000         0.217     0.291\n",
       "stock_VERIZON COMMUNICATIONS                    0.2472      0.023     10.825      0.000         0.202     0.292\n",
       "stock_VISA INC                                  0.4192      0.024     17.309      0.000         0.372     0.467\n",
       "stock_WALGREENS BOOTS ALLIANCE INC              0.2793      0.018     15.194      0.000         0.243     0.315\n",
       "stock_WALMART INC                               0.3251      0.031     10.594      0.000         0.265     0.385\n",
       "stock_WALT DISNEY CO                            0.3131      0.014     21.827      0.000         0.285     0.341\n",
       "stock_WELLS FARGO & CO(NEW)                     0.7175      0.062     11.586      0.000         0.596     0.839\n",
       "stock_WILLIAMS COMPANIES                        0.2522      0.019     13.501      0.000         0.216     0.289\n",
       "stock_WYNN RESORTS LTD                          0.2461      0.022     11.047      0.000         0.202     0.290\n",
       "stock_YELP INC                                  0.3195      0.027     11.882      0.000         0.267     0.372\n",
       "==============================================================================\n",
       "Omnibus:                       26.157   Durbin-Watson:                   1.605\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               26.187\n",
       "Skew:                           0.059   Prob(JB):                     2.06e-06\n",
       "Kurtosis:                       2.970   Cond. No.                     1.95e+19\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The smallest eigenvalue is 9.91e-12. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target = 'Returns20D'\n",
    "model_data = pd.concat([y[[target]], X], axis=1).dropna()\n",
    "model_data = model_data[model_data[target].between(model_data[target].quantile(.025), \n",
    "                                                   model_data[target].quantile(.975))]\n",
    "\n",
    "model = OLS(endog=model_data[target], exog=model_data.drop(target, axis=1))\n",
    "trained_model = model.fit()\n",
    "trained_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Models for Prediction: sklearn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since sklearn is tailored towards prediction, we will evaluate the linear regression model based on its predictive performance using cross-validation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Custom Time Series Cross-Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our data consists of grouped time series data that requires a custom cross-validation function to provide the train and test indices that ensure that the test data immediately follows the training data for each equity and we do not inadvertently create a look-ahead bias or leakage.\n",
    "\n",
    "We can achieve this using the following function that returns a generator yielding pairs of train and test dates. The set of train dates that ensure a minimum length of the training periods. The number of pairs depends on the parameter nfolds. The distinct test periods do not overlap and are located at the end of the period available in the data. After a test period is used, it becomes part of the training data that grow in size accordingly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "def time_series_split(d=model_data, nfolds=5, min_train=21):\n",
    "    \"\"\"Generate train/test dates for nfolds \n",
    "    with at least min_train train obs\n",
    "    \"\"\"\n",
    "    train_dates = d[:min_train].tolist()\n",
    "    n = int(len(dates)/(nfolds + 1)) + 1\n",
    "    test_folds = [d[i:i + n] for i in range(min_train, len(d), n)]\n",
    "    for test_dates in test_folds:\n",
    "        if len(train_dates) > min_train:\n",
    "            yield train_dates, test_dates\n",
    "        train_dates.extend(test_dates)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select Features and Target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to select the appropriate return series (we will again use a 10-day holding period) and remove outliers. We will also convert returns to log returns as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 45114 entries, 2014-01-02 to 2015-12-16\n",
      "Columns: 183 entries, Returns10D to stock_YELP INC\n",
      "dtypes: float64(183)\n",
      "memory usage: 63.3 MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "target = 'Returns10D'\n",
    "outliers = .01\n",
    "model_data = pd.concat([y[[target]], X], axis=1).dropna().reset_index('asset', drop=True)\n",
    "model_data = model_data[model_data[target].between(*model_data[target].quantile([outliers, 1-outliers]).values)] \n",
    "\n",
    "model_data[target] = np.log1p(model_data[target])\n",
    "features = model_data.drop(target, axis=1).columns\n",
    "dates = model_data.index.unique()\n",
    "\n",
    "print(model_data.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    45114.000000\n",
       "mean         0.001159\n",
       "std          0.045740\n",
       "min         -0.157448\n",
       "25%         -0.025013\n",
       "50%          0.002817\n",
       "75%          0.028880\n",
       "max          0.146139\n",
       "Name: Returns10D, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_data[target].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = pd.IndexSlice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OLS Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use 250 folds to generally predict about 2 days of forward returns following the historical training data that will gradually increase in length. \n",
    "\n",
    "Each iteration obtains the appropriate training and test dates from our custom cross-validation function, selects the corresponding features and targets, and then trains and predicts accordingly. \n",
    "\n",
    "We capture the root mean squared error as well as the Spearman rank correlation between actual and predicted values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "nfolds = 250\n",
    "lr = LinearRegression()\n",
    "\n",
    "test_results, result_idx, preds = [], [], pd.DataFrame()\n",
    "for train_dates, test_dates in time_series_split(dates, nfolds=nfolds):\n",
    "    \n",
    "    X_train = model_data.loc[idx[train_dates], features]\n",
    "    y_train = model_data.loc[idx[train_dates], target]\n",
    "    lr.fit(X=X_train, y=y_train)\n",
    "    \n",
    "    X_test = model_data.loc[idx[test_dates], features]\n",
    "    y_test = model_data.loc[idx[test_dates], target]\n",
    "    y_pred = lr.predict(X_test)\n",
    "    \n",
    "    rmse = np.sqrt(mean_squared_error(y_pred=y_pred, y_true=y_test))\n",
    "    ic, pval = spearmanr(y_pred, y_test)\n",
    "    \n",
    "    test_results.append([rmse, ic, pval])\n",
    "    preds = preds.append(y_test.to_frame('actuals').assign(predicted=y_pred))\n",
    "    result_idx.append(train_dates[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_result = pd.DataFrame(test_results, columns=['rmse', 'ic', 'pval'], index=result_idx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have captured the test predictions from the 250 folds and can compute both the overall and a 21-day rolling average:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oS89rK+a2OGp2l54nJykhFxEZYNLLz07IAZwcnLhp3JWYMLHh+Nc97qeotpQ/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RAAAg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\n2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XLEuu17Yp32PbFO+45Yl31PrNMrQ6zXK0Os1ytDrNef9WlB7uzsbLgiDlBTU4OTk5Ph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\nBEEQBEEQBCP4P5WwsHZojBouAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcdb8e52750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2)\n",
    "rolling_result = test_result.rolling(21).mean()\n",
    "rolling_result[['ic', 'pval']].plot(ax=axes[0], title='Information Coefficient')\n",
    "axes[0].axhline(test_result.ic.mean(), lw=1, ls='--', color='k')\n",
    "rolling_result[['rmse']].plot(ax=axes[1], title='Root Mean Squared Error')\n",
    "axes[1].axhline(test_result.rmse.mean(), lw=1, ls='--', color='k')\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the entire period, we see that the Information Coefficient measured by the rank correlation of actual and predicted returns is weakly positive and statistically significant:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KAMCHDW2ij2clX5bIsmxzevjAFK4GocvjRzga38ZEOJZECcccifLPtBomrjEl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\niRdBEAQRcZB4EQRBEBHH/wfKaRcPhOky/gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcdb8e6eb90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds_cleaned = preds[(preds.predicted.between(*preds.predicted.quantile([.001, .999]).values))]\n",
    "sns.jointplot(x='actuals', y='predicted', data=preds_cleaned, stat_func=spearmanr);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regularization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the ridge regression, we need to tune the regularization parameter with the keyword alpha that corresponds to the λ we used previously. We will try 21 values from 10-5 to 105 in logarithmic steps."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Ridge Regression: L2 Penalty"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The scale sensitivity of the ridge penalty requires us to standardize the inputs using the StandardScaler. Note that we always learn the mean and the standard deviation from the training set using the .fit_transform() method and then apply these learned parameters to the test set using the .transform() method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20\n"
     ]
    }
   ],
   "source": [
    "nfolds = 250\n",
    "alphas = np.logspace(-5, 5, 11)\n",
    "scaler = StandardScaler()\n",
    "\n",
    "ridge_result, ridge_coeffs = pd.DataFrame(), pd.DataFrame()\n",
    "for i, alpha in enumerate(alphas):\n",
    "    print i, \n",
    "    coeffs, test_results = [], []\n",
    "    lr_ridge = Ridge(alpha=alpha)\n",
    "    for train_dates, test_dates in time_series_split(dates, nfolds=nfolds):\n",
    "\n",
    "        X_train = model_data.loc[idx[train_dates], features]\n",
    "        y_train = model_data.loc[idx[train_dates], target]\n",
    "        lr_ridge.fit(X=scaler.fit_transform(X_train), y=y_train)\n",
    "        coeffs.append(lr_ridge.coef_)\n",
    "\n",
    "        X_test = model_data.loc[idx[test_dates], features]\n",
    "        y_test = model_data.loc[idx[test_dates], target]\n",
    "        y_pred = lr_ridge.predict(scaler.transform(X_test))\n",
    "\n",
    "        rmse = np.sqrt(mean_squared_error(y_pred=y_pred, y_true=y_test))\n",
    "        ic, pval = spearmanr(y_pred, y_test)\n",
    "        \n",
    "        test_results.append([train_dates[-1], rmse, ic, pval, alpha])\n",
    "    test_results = pd.DataFrame(test_results, columns=['date', 'rmse', 'ic', 'pval', 'alpha'])\n",
    "    ridge_result = ridge_result.append(test_results)\n",
    "    ridge_coeffs[alpha] = np.mean(coeffs, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rmse</th>\n",
       "      <th>ic</th>\n",
       "      <th>pval</th>\n",
       "      <th>alpha</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4956.000000</td>\n",
       "      <td>4956.000000</td>\n",
       "      <td>4.956000e+03</td>\n",
       "      <td>4.956000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.046201</td>\n",
       "      <td>0.095743</td>\n",
       "      <td>2.462496e-01</td>\n",
       "      <td>5.291005e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.018699</td>\n",
       "      <td>0.148136</td>\n",
       "      <td>2.990978e-01</td>\n",
       "      <td>2.128608e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.028501</td>\n",
       "      <td>-0.422446</td>\n",
       "      <td>1.374229e-16</td>\n",
       "      <td>1.000000e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.038269</td>\n",
       "      <td>-0.005162</td>\n",
       "      <td>4.278807e-03</td>\n",
       "      <td>1.000000e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.043257</td>\n",
       "      <td>0.095800</td>\n",
       "      <td>9.506434e-02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.050236</td>\n",
       "      <td>0.201505</td>\n",
       "      <td>4.138598e-01</td>\n",
       "      <td>1.000000e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.466332</td>\n",
       "      <td>0.576016</td>\n",
       "      <td>9.991168e-01</td>\n",
       "      <td>1.000000e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              rmse           ic          pval         alpha\n",
       "count  4956.000000  4956.000000  4.956000e+03  4.956000e+03\n",
       "mean      0.046201     0.095743  2.462496e-01  5.291005e+08\n",
       "std       0.018699     0.148136  2.990978e-01  2.128608e+09\n",
       "min       0.028501    -0.422446  1.374229e-16  1.000000e-10\n",
       "25%       0.038269    -0.005162  4.278807e-03  1.000000e-05\n",
       "50%       0.043257     0.095800  9.506434e-02  1.000000e+00\n",
       "75%       0.050236     0.201505  4.138598e-01  1.000000e+05\n",
       "max       0.466332     0.576016  9.991168e-01  1.000000e+10"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge_result.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Significance of Information Coefficients - p-value Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vv/9+vPPOO9HQ0NDpMY27N/Z4kFJbPXBAUQ8Q1S2fz3teKRvrjXKz5ign641yyufzRR1X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\nIAniBQAASIJ4AQAAkiBeAACAJPwv1MmMO9sFjRIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcdb7341350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(ridge_result.pval, bins=20, norm_hist=True, kde=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "ridge_result_sig = ridge_result[(ridge_result.pval < .05) & (ridge_result.alpha.between(10**-5, 10**5))]\n",
    "ridge_result_sig_alpha = ridge_result_sig.groupby('alpha')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "ridge_coeffs_main = ridge_coeffs.filter(ridge_result_sig.alpha.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ridge Path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now plot the information coefficient obtained for each hyperparameter value and also visualize how the coefficient values evolve as the regularization increases. The results show that we get the highest IC value for a value of λ=10. For this level of regularization, the right-hand panel reveals that the coefficients have been already significantly shrunk compared to the (almost) unconstrained model with λ=10-5:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 4956 entries, 0 to 235\n",
      "Data columns (total 5 columns):\n",
      "date     4956 non-null datetime64[ns, UTC]\n",
      "rmse     4956 non-null float64\n",
      "ic       4956 non-null float64\n",
      "pval     4956 non-null float64\n",
      "alpha    4956 non-null float64\n",
      "dtypes: datetime64[ns, UTC](1), float64(4)\n",
      "memory usage: 232.3 KB\n"
     ]
    }
   ],
   "source": [
    "ridge_result.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_ic = ridge_result_sig_alpha['ic'].mean().max()\n",
    "best_alpha = ridge_result_sig_alpha['ic'].mean().idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Q6B4FYKg7k/P5meyj87mIiAxXZ3pOH9RAPmfOHJ588kluuukm9uzZQ1JSUnCohm6mafb5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S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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcdae323f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, sharex=True)\n",
    "\n",
    "ridge_result.groupby('alpha')['ic'].mean().plot(logx=True, title='Information Coefficient', ax=axes[0])\n",
    "axes[0].axhline(ridge_result.groupby('alpha').ic.mean().median())\n",
    "axes[0].axvline(x=ridge_result.groupby('alpha').ic.mean().idxmax(), c='darkgrey', ls='--')\n",
    "axes[0].set_xlabel('Regularization')\n",
    "axes[0].set_ylabel('Information Coefficient')\n",
    "\n",
    "ridge_coeffs_main.T.plot(legend=False, logx=True, title='Ridge Path', ax=axes[1])\n",
    "axes[1].set_xlabel('Regularization')\n",
    "axes[1].set_ylabel('Coefficients')\n",
    "axes[1].axvline(x=ridge_result.groupby('alpha').ic.mean().idxmax(), c='darkgrey', ls='--')\n",
    "fig.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 10 Coefficients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The standardization of the coefficients allows us to draw conclusions about their relative importance by comparing their absolute magnitude. The 10 most relevant coefficients are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAAAAAAYhlAMAAAAAYBBCOQAAAAAABiGUAwAAAABgEEI5AAAAAAAGIZQDAAAA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\noUOHNGTIELVq1UqS9Oeff6pXr15ydXXVxYsXc9WefV7x8fE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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcdb7f19f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_coeffs = ridge_coeffs_main.loc[:, best_alpha]\n",
    "model_coeffs.index = features\n",
    "model_coeffs.abs().sort_values().tail(10).plot.barh(title='Top 10 Factors');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CV Result Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAHgapQgAAACAp1GKAAAAAHgapQgAAACAp1GKAAAAAHjaEDdWWlpaqmAwKJ/P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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcdb6a27a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(y='ic', x='alpha', data=ridge_result_sig)\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lasso Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The lasso implementation looks very similar to the ridge model we just ran. The main difference is that lasso needs to arrive at a solution using iterative coordinate descent whereas ridge can rely on a closed-form solution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1 2 3 4 5 6 7 8 9 10 11 12\n"
     ]
    }
   ],
   "source": [
    "nfolds = 250\n",
    "alphas = np.logspace(-8, -2, 13)\n",
    "scaler = StandardScaler()\n",
    "\n",
    "lasso_results, lasso_coeffs = pd.DataFrame(), pd.DataFrame()\n",
    "for i, alpha in enumerate(alphas):\n",
    "    print i,\n",
    "    coeffs, test_results = [], []\n",
    "    lr_lasso = Lasso(alpha=alpha)\n",
    "    for i, (train_dates, test_dates) in enumerate(time_series_split(dates, nfolds=nfolds)):\n",
    "        X_train = model_data.loc[idx[train_dates], features]\n",
    "        y_train = model_data.loc[idx[train_dates], target]\n",
    "        lr_lasso.fit(X=scaler.fit_transform(X_train), y=y_train)\n",
    "        \n",
    "        X_test = model_data.loc[idx[test_dates], features]\n",
    "        y_test = model_data.loc[idx[test_dates], target]\n",
    "        y_pred = lr_lasso.predict(scaler.transform(X_test))\n",
    "\n",
    "        rmse = np.sqrt(mean_squared_error(y_pred=y_pred, y_true=y_test))\n",
    "        ic, pval = spearmanr(y_pred, y_test)\n",
    "        \n",
    "        coeffs.append(lr_lasso.coef_)\n",
    "        test_results.append([train_dates[-1], rmse, ic, pval, alpha])\n",
    "    test_results = pd.DataFrame(test_results, columns=['date', 'rmse', 'ic', 'pval', 'alpha'])\n",
    "    lasso_results = lasso_results.append(test_results)\n",
    "    lasso_coeffs[alpha] = np.mean(coeffs, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rmse</th>\n",
       "      <th>ic</th>\n",
       "      <th>pval</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.000000e-08</th>\n",
       "      <td>0.045714</td>\n",
       "      <td>0.108370</td>\n",
       "      <td>0.255438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.162278e-08</th>\n",
       "      <td>0.045713</td>\n",
       "      <td>0.108384</td>\n",
       "      <td>0.255490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.000000e-07</th>\n",
       "      <td>0.045710</td>\n",
       "      <td>0.108429</td>\n",
       "      <td>0.255493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.162278e-07</th>\n",
       "      <td>0.045699</td>\n",
       "      <td>0.108550</td>\n",
       "      <td>0.255804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.000000e-06</th>\n",
       "      <td>0.045667</td>\n",
       "      <td>0.108794</td>\n",
       "      <td>0.255666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.162278e-06</th>\n",
       "      <td>0.045572</td>\n",
       "      <td>0.109276</td>\n",
       "      <td>0.254775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.000000e-05</th>\n",
       "      <td>0.045365</td>\n",
       "      <td>0.110997</td>\n",
       "      <td>0.247775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.162278e-05</th>\n",
       "      <td>0.045343</td>\n",
       "      <td>0.110751</td>\n",
       "      <td>0.244619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.000000e-04</th>\n",
       "      <td>0.044766</td>\n",
       "      <td>0.103843</td>\n",
       "      <td>0.248817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.162278e-04</th>\n",
       "      <td>0.044462</td>\n",
       "      <td>0.095700</td>\n",
       "      <td>0.238646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.000000e-03</th>\n",
       "      <td>0.044479</td>\n",
       "      <td>0.059093</td>\n",
       "      <td>0.251200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.162278e-03</th>\n",
       "      <td>0.044529</td>\n",
       "      <td>0.050669</td>\n",
       "      <td>0.190781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.000000e-02</th>\n",
       "      <td>0.044537</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  rmse        ic      pval\n",
       "alpha                                     \n",
       "1.000000e-08  0.045714  0.108370  0.255438\n",
       "3.162278e-08  0.045713  0.108384  0.255490\n",
       "1.000000e-07  0.045710  0.108429  0.255493\n",
       "3.162278e-07  0.045699  0.108550  0.255804\n",
       "1.000000e-06  0.045667  0.108794  0.255666\n",
       "3.162278e-06  0.045572  0.109276  0.254775\n",
       "1.000000e-05  0.045365  0.110997  0.247775\n",
       "3.162278e-05  0.045343  0.110751  0.244619\n",
       "1.000000e-04  0.044766  0.103843  0.248817\n",
       "3.162278e-04  0.044462  0.095700  0.238646\n",
       "1.000000e-03  0.044479  0.059093  0.251200\n",
       "3.162278e-03  0.044529  0.050669  0.190781\n",
       "1.000000e-02  0.044537       NaN       NaN"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_results.groupby('alpha').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rV69AlvkfRUREOPK8TmG87sZ43c+0MTNeuIlp/WW8sMvZgmfQQ9Hw4cP1wQcf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\nIiIiIiIiTfsfSffGvKLG4zQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcdaec29650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(y='ic', x='alpha', data=lasso_results)\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-validated information coefficient and Lasso Path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As before, we can plot the average information coefficient for all test sets used during cross-validation. We see again that regularization improves the IC over the unconstrained model, delivering the best out-of-sample result at a level of λ=10-5. The optimal regularization value is quite different from ridge regression because the penalty consists of the sum of the absolute, not the squared values of the relatively small coefficient values. We can also see that for this regularization level, the coefficients have been similarly shrunk, as in the ridge regression case:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0BvUOg9IFb2527w39cSS1EFv2n8fw+HBMGh7j7SER0VUwOHcihIDn4XAAQPPf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+zwXiJHOI8Vl1qvshCjIV91QT2pnYlp9XZMOAZXfbQeZSVdiBz9ahQMLY7r3KmagX1dkKrHzZ\nKW4vHlc0HoVUYDkO+9qn+pxW7npz0ZVEVwpdSnRpEx6N4Y8NE4jH8CdGCSTjlAf9VNZUUtNUz6z5\ncwk1riThSNbv7mLrayc4/txBjKSNDvgMQcksL63xw1TsfBH2JAprKcPgQGMJO1sF7bNMDNPDuXXL\nuKV5NfMrF5K0JbG0zdaeYWIZm43DcOC5nTgK7ISF3pfmU5fNo+vF5zj8o0epTCeJVlTTfvWHuf3a\nywh6DAZjKf79/q1s2tNDOODh659cwQVLxhTbYk/q8+ceZ27rMQYDi/ljbCEWHlpLA9yypImmSACp\nFD96cT9bYnH0lEPlQJy+OWEQgo/NKeHpp7o41jVC3dIKrOoAImnTs6mHZi3FdYcfpvGSC6m5+qpJ\n93xT1yCbu4eYWxbk8tlVZNI2D/x6C8cOZWO63/75C2mYPbNQzVIpfrrtGH2JNFfNmcWKWaVv7Avh\n4jJDplXIn332We677z48Hk+hLhQK8Z3vfIe//Mu/dBVyFxcXl3cQQghqK4J09I+ilHLPw73LWbVq\nFd/4xjfo7e3lnnvu4U9/+hOrV68+22LNiO1974wXB2eHAMf70mdbiNMiHxEs67wtX86+YtCK60Xe\npVwuL8i6dhMit9NeNCb375dW1FcAqWSC0nAIXQgMLX9pk/NCoKMQloVmZRCZDCKdRkunIJWCVBKR\nSEIyjoonID4K8VFkbBQScXTbQpMS3bHRHAddOggpJ72y8FZXEZo3j/D8eYTnX0ywtQXd5wMgnrFZ\nv6eLLc9uYPTIEGbcBkDXBaEqg9nD+6jc+zLaXhvL9DDiDxAvq+BYTSXHG/0MlgVB81Pmr2a+txJd\nC9CZlPxku42jdk75JGTSZuTwMItrSmhqFPT9j/+bup4OLNPDpgs/wPKPXc/nW2oQQrBpTw//dt8W\nhkbTrJhfxVduXkV5xFeYbcurx3MhxSTLl+6joXGEl3gf22O1+MjwsUXVXDK7Hk0IooMJvv/cHnq9\nAmPUot7jcKw+ggeLT7XV8PjT3ew6MkDNskpklR/i+iusvwAAIABJREFUFt2bellcKrjy1d9SOruR\nOV+4c9L/W7G0xa92tWNqgtuXNjMymOT+ezbS0zkCwJKVdTNWxgGePNzDtt5hFlWEuX5+3YzHubi8\nUaZVyH0+3zhlvLheO4lDCRcXl3cHjzzyCJlMhnPeAw6E3ivUVAY43DnMYCw97oeVy7uPr3zlKzzx\nxBP4fD66u7u54447uOKKK862WDPiv66ZbJJ6ck437vdpTn+aq04//8l30WfCrl27WLKkbdJee/Ga\n40NtT+0hvihU9rg5pvULL4r9rI9lBIL8kVwhcr7Rp1C+3yhKKZASadsoy0Y5djZv2yjbKeR3b9lN\na7AOe3QUOzY6Ps1dVq4sp4n7PRXCNDHDYYxwCKN+FkYolM2HQpjhMHowiJkrG+HwWHtgvCfvWMbm\npe3tvLalg5GOETRLIj06TokHu9FHIDOIYY8y7PPzau18UitXkPIFsufLJ5A3pE44kEiAV7cIewya\nSvyEPQZhj0nQ0BkaSuJr345tSx45EGTZudXUvfI083duRFOKI62LOPL+q7njkpU0RPxkLId7HtnF\nIy8ewdA1Pn3tEq5d24qWe9BKKp59Yi8vPn0Qj0eyavkOAlU6v0p/gBHCLBKHuPW886iubEApxfqX\nj/Lr4z2kwyb+tIMe1jimTEpEnDtXNvPY8wO8uqeb6uVVUOFDjWTofa2X85rDvO/ZH+MN+ln4d3+D\nPoX/ql/vPkEsY3PjwnrSvXF+8bNNJEYzlFcGifbHOfei2TN+xh0ZeGRfJ6U+k8+smI3mvrR2eQuZ\nViFPJBIkEgkCE/4xGR4eJh53TR5dXFxc3mkUnyN3FfJ3J/kwpe3t7bS1tdHW1lZoa29vf0d4Wq+Z\n6pfJdE6+VOGPqRqmmOZk80zTUAirNaFzIaxW8bxqXEJRvVIT2ibMo9TkurFiNhMZ6ic80JcL1SVR\nMut6DakKSqsq2IkrlJJjbfk6KUEpZKGcnasQKmxcHUVzyKJ5c2s6Y8qwchwcyyrklW3j5BTmQr5Q\n7xTy0nbGFGzHRtqyqM1G5vL5tw7jzP9F/jZltX+FYIuWDfk1do2VRSCAXlqJ1tCM5vejBYOIQBA9\n4EfzBxABP5rfj/D7ET4/ms+L8PlAN5CApRRplTVtLjbLl7n7qcjl4wonNkLcihJNZjgxlGAwlsKS\nCmVoqCoPVFVO8X0rKeR0x0bIJBZRpJ0CJ0O5HuSc2a20lFcR9phEvCYhj0HYY+DJOR5TSnHoxDBP\nbzzOn7Z1kNLgQ/MUmqGxqiLKeb+8n2BilFikjJfX/AWzLziPr7c14jV0jnaN8N9/sYlj3TEaZ4X4\n2i3n0lo/JpNtOTx431Z2be0kGExz7srtDAQreSB9PmV6ivfzJ1a3XU5l5WwG+kb5zR93sK1Cxwmb\nBIUg7tXRlMN5nsPcfP4l/OHP/Ty1uZ2qFVVoZT7kUJreLX1ctnQWa/58D5adYf43voavpmbSndrS\nPcTGrkFaS4OU9yT5+R+yVgEf/NBinn18L+WVQRpbZrY7Hk1m+D9DoAnBnStbiXjNUw9ycTmDTKuQ\nX3fddfzVX/0V//iP/8js2bMB2Lt3L//0T//EHXfc8VbJ5+Li4uJyhijEIh+I09bqxlR9N/Kd73yH\n73//+9x+++3A2O5k/pjC008/fTbFmxFf+j/bz7YIBdSZOod+hqZRlMHWrhnOPXXD1O8etNM2HcjH\nucYU8G7RX2wgBsTO/JlzoQk0R2ImMgTjg5SM9BNIxvAlE9krk8KsiXC8wmJjsBfbcNCUTjhaTUOi\nhduuvYK582dNLbZU7O4a4pmdnezoHCKBwggYVC4JUDIcxYuNkba49KkHkLrO1nPWsu+cNdy8ooUL\n6ytQSvHQC4f434/sxrIlV180mzs+NOa4DSA+mub+ezZy4uggZWUjnLtiJ9v0RexmGZeWdtISe47q\nxvMpqz2XF58+wJOvHKFnSTnK1NAFxJWill6uKGnn/HNv5tFXuvn9nw9SubIKvcSLM5Cib1sf11w4\nm4u2PkCsr4+mT9xE2TmrJn3eeMbmFzuPYwDz2hM8tmE//oDJjbefy0DfKLYtWbG6cUbWGUop7tl+\njJQS3Ly4njllwdf9jF1cXi/TKuR33HEHHo+H22+/ndHRUaSUVFRU8LnPfY7rr7/+rZTRxcXFxeUM\nUNghdx27vWv5/ve/D8AzzzyDlLJwxMyyLEzznaE1VWSSpzfgTbYsndroeyYyiOlLJ51umkYB9rjn\nKCZsFE+wOS8yM8+Xx60wzs59wqcU40rjzN/H2nK+KASFM+AIkTvTLcbKWj6vTarPziQmvQuY8DEQ\nE+/lVCb6CIaGBqkoL8+ePy+cOc+a1Y8vi9xZdNAQuXPqFFKNCWUhcKQiZTskLIfRRIbReIZ4IkMy\nbmElM9gpGzIOWkZmL6nQrGzeL0dp6N1NU3Q/psoULCCErsOiVvbP8/J4cISkMYRA0BKcjdpXiq+7\nkkWLGrj2tuUEQl6kUgwkM3TEknTEUrQPJzjaPYgc6KFkOEpkaICLh6KUDA9QMjSAaWUA6L5uHQD9\nzXN4/sIrKGmo5+9XtlAT8jEYS/Gv923htb29RIIe/u72laxePH5Hur8nxq//cwODAwnqantZuPgQ\nz4gLqaldxpdD7QwffpZgaTNa4H385H++wBHHYmB5BfnzDCYWa7XNnFflZc7y23h5Vz8/fXwPFauq\n0cMerN4kAzv7ufHyeazte43O7dspO+9cGm78CFNx354TjMbTLDwUZ3dHjOqaMB//1GrKKgI8/ege\nhIBl5zZMOXYiG7oG2TsQo8mjuKy5akZjXFzONCeNQ37LLbdwyy23MDo6ihCCYNB9a+Ti4uLyTqUQ\ni7w/cYqeLu90nnzySf74xz9y9913A9n/zz/1qU9x5ZVXnmXJTs1/u/Gysy3C25bNmze7fj1OwubN\nmzlnZctpj8s4ksFUhoFEhp5onL7oKNFokthQkkQsTWY0g0pmY4AbaQdRFMnMk7vyCF2iYeFxktQM\nHaZ+cD8+kTXrz7brGIvmcqwlzNOhXqJiGIDGSC0XN67G2Rth97MDCL/BsnVzCTZG+M3BTga6ekh2\ndOKP9lEyFCUyPEDL0ABLR0cmfR5hGPjravHV1ZGurMIJBskowfN/8XEuba7iY4saMHWNjbu7+bf7\ntzA8mmHVgmruumklZROOMx092M/992wknbKZN+cYVa1RXvat46NLl1Ineti/+RHQSjh84iI2/fZl\nhptDjMwZMxVfbHZwnnyFhvqlNC++kXha8qMHd1KxqhojZJLpihPdHeX2axZzqa+fff/xR3y1Ncy/\n68uIKXxWbe8dZvO+Hup3RIklbBYsqeH6m1fi9Rn0dcfoOD7E3IXVREr8p3zuScvht3s6MDXBmrDr\n7NTl7HFShTxPKBR6s+VwcXFxcXmTqSz1Y+jCDX32HuCee+7hxz/+caH805/+lE9/+tPvCIXcxeVM\nYjmSwZTFQCJNbzRO70Cc6GCSkaEkyViadE7ZNlIOetpBTLDpF2QdqCkUQpPowsJDCp89SiA1Qjg5\nSCA9gs+O45UpDK+JZhgoKXFyPpeUpuFvW0jXvHKejQxw3B4EBin1RbiyYQ3zqlYyNGSyYcNhRLyH\nYP0wodggow+sRx8eoGU4yrycQl9M3AyQqGuhet5squbOxl9fh7++jmggwobuITZ0RulLZFgTP4oA\n7lzVyjm1ZaQth7sf3M6j67OO2/7LdUtYt2bMcVuebRvbefg321DKYfmS/VCnI1tv5ytzmpDpIfa8\n8gt6+8rYd3AFQ8MdDJ9XzWgka8FR7tV4Hy8yyzlGTevl1M29EiEEP3t8J+aCEoyQSerEKMP7B/n8\nR5ZxaaPJtq/9v2heLwu/8bcYocmbgAnL5ldP7aV6ax84irUfnMelVywoWFts3dgOwIrVM/OV8dCB\nLobTFtfOqyUy0jmjMS4ubwYzUshdXFzee6xbt47NmzefbTFcziC6JqguC7gm6+8BlFKEw+FCORQK\nubs/Lu9IpFJYjiQjc6kjyUiJ5UhSlkMqbZNO2yTTNvvaM2zu2sHIUJLESJJM3EIls4r2VMo2TFC2\nNQuPTOGzRgmkhgknowTTMQKGg99w0HUdAOXYOOk0MjnZU7tM2kgAIQguWkB0US0vlo2wM9mFpoGP\napaaK6gYFejtg/DyHnqG11MyNMCVidHJ85kmsUAZnSrIgBFhxFdKQ9sczr9kGRcsbUTPKaNDKYuN\nXVFeORjleE659Ooa59eVsapuDsnjBzmntowjncN87xebae+J0VQT5mu3nENLXcm4NZVSPPXYXl5+\n5iCGYXPuyl2k61u56JwPUxHwIR2LnRt+wabNjXR2VWOHIPr+evIB+S6tMVkQ/S1CpmhceD3VTRcD\ncKB9iFfjcbwVPpInRokdGOIrN69i7aIKtv/N3yFTKeZ/7asEm5sm3QelFD++fwv+Lb1ohsZHblvF\n4uVjockcR7J98wn8AZP5bVOftS/mxEiSZ471UhXwcmXrLLZvdRVyl7OHq5C7uLi4vIeoqQzy2t5e\nEimLgO+dcabY5fRZsmQJd911F6tXr0YpxQsvvMCSJUvOtlgu7zCy3sOzypAkl+bqpFLYUpFxJJaU\npCxJKm2RymQV5FTGIZ1xyGTsbJq2yaQtrIyNZdnYGYltOzi2zF0K6UiUo5COQjlZT+5IEI5CyNxV\nnJ/SQ11/Iechp2zr45XtYGqQiBUnrGcI6A4+YaGhUFZW0XbicZTjjJvVyV0AmseDp6Ics7UcUVmF\nVRImEQow4jMY9goGhUN/epRYdz+lfUkqD8H1w2Eiw/1Ehg+gy/FzKyDlC6PmLKB6fgsxfynbBjVe\n7LDoc3wgBItbyrn83CbWLK8j6M/+2520HF7rHOLVjih7B2Iosse2l1ZFOL+unBWzSvAa2ZcIG48r\nHvxz1nGb7UjWXdzCX36oDa+pj5PFytj8759tpGtvPwF/klXn7KGi7YMsn39R9jshJc8+/CAbXqkn\nY5vIFVV0VWSN9oOmzqdaHazDv0AIjZZlt1JWsyx7/xzJ/3hxL94KH6m+JPGDQ/zdbedxwZIa9n3n\neyRPdFB33YeoWnvxpCdqZWx+fu8mhvf0IfwGd3zuQhoaS8f1Obi3l3gszeo1LRiGPmmOcfdbKX61\nqx2p4ObFWfN9F5ezySkV8gMHDvDb3/6W4eHhceFCvvvd776pgrm4uLi4nHmKQ5/NaSg9RW+Xdyr/\n8A//wEMPPcT27dsRQvChD32Iq6666myLNSN+8ttXgVOHKFPTxyobHyVNnbz3dMvkZSiOaKayf6AK\n06oJkc7G+quxThOioI3NMTHKWaHvxPZcJ8uyePLlZ7L9ZE6OvIy5NL9ufq3x5TGZx0VqK150QgQ3\nVE7xnaJcUIynVY5njgCmUqMUKusEHgVCIYRCCInQJUJJNCSactCUg8+KEyFFRKUIiwwBLYNXpXHS\nNmlbkkqlyTgS2zBxDJ1RzWBE03F0P1LoKJ+GqPCAYSJ0PXuGWUmUdFC2g7AthG2j2RaabWP0DmJ0\n9mHYFoZtEbAtwrZF0zRfKun1QUMD3vo6CFWw80CKqBPg/2fvvMPkusr7/7ll+s7u7GxvWvXemyVX\nXDA4NhiCAReMIRgbDL9AyANOTEKeJCQkQIJJcEI1BHABXDAxNgRs3K1iyVr1tlppe52d3elzy/n9\ncadu0a5lSSuL+3meyz33nPec+86dQevvPee8b/WSOWy6djlbD/bzwx0d9IesOB/V5X5uXD+LK9Y3\nUZeJAaKbJrv6wmzpCrG7fwTNtO41L+Djgvog6+sC+AvSdgkhaO0a4YHnBmnt6SJQ4uIzN65h/ZLx\ns8jH+0Z54AdbMIZSlAdGWL2+nVWbP0ZpwAqQNjwU4/GfvkhnuwstqBJbW0sk81kXlvu4qaaHvqO/\nRlHdzFv9UfzBubmx733uIHqZk/RICqN1hK986mIWNwfpfPRxhl7dSunyZcy+7dZxPo0MJ3j4h9vo\n6xolVebktts30lg//m/Xrm3WcvVVG6Zerr6lO8SR4Sira8pYUV02pb2NzZlmSkH+2c9+lmuuuYYl\nS5acDX9sbGxsbM4g+dRncVuQn4dk85B3dnaydu1a1q7Npwzq6up6S+Qh79raN33jN7wK/w2qxgJz\nqfhiUvO83ZhQ4GKs5ZjhpInbxmJk7mJFObdKUjZqt5QX/1LWrCh5W4HalsYUpcxsssh2Fplw5tmx\ns2OJnAcoJrJsImGJZFmYyAgkDGQECnnRrGAiC6u/TNbORBICOfMmQEJYn0UIpGzecwQY+Tzqkx6A\nZJrIpgnCJGYYJA0dxTBQdQ1F13DpGp6TvYF5g+iKgqGqGA4HhtuB7vCBy4nicqG6vZTU1RFsnkXF\n7Ca8jQ04AgFMQ/Ds0wd59blWTCfULK9lTyTJz/7zRQDcToUrNzRx5fpZLJtbgSxLmEJwOBRla1eI\n13qHiWvWDHutz8UFDUEuqA9S5XUV+RYaTfLcjk6efa2dE70RANYtruYzN66h3F8cuC1lmDz2WhuH\n/vcgcsKkrrafjRemWLrhz3E4SzANky0vtPHcbw6QQiK1xstQsDz33N85p5oN7KTv6Is4XGUsWHs7\nHn8+Uvvjezo4mEqix3XEoTD/+ZeXU1HmIbyrhRM/fRBnRZBFn/+cFXW+gI62ED//n9eIRVJE671s\n/pNFLKwvH/c9xCIpjuzvo6a+lLrGkwvsuKbziwNdOGWJG5dOLxK7jc2ZZkpBXllZyac//emz4YuN\njY2NzRmmrsILYAd2O08pzEMuSVIu//hbKQ/5VUf+Z6ZdsDkPMBQFQ3VgOhwYbhea6sNQFTRVQVMl\n0jIkFRNNAV0BTZXQFIGmgq6YmKqE2+vDV+LDX1JKoCxIoLSCYGkVFYFqKgPVeJxTR/IuZGggyqM/\n2cGhrhEiLoVBw0Tb3W2l6ZpfyZUbmti8oh6Py/rP865Igq1dIbZ2DxNKWinMylwOrppdwaaGILNK\nPUWxIdKawda9vTzzWjuvH+rHFKAqEptX1DG7PM1N795UZC+EoKV/hJ+/eATX9l5kHebPbeeiyxuY\ntfhdSLJCT2eYJ3+xm+7OEVKNTkYWlJGWrSXqMnDr8kbqw//HQG8Lbl8NC9bdjtOdf9n7ZEsHv+7s\nR2gmqZZBvvXZtxEs85Ds7+fQ17+BJMssvvvzOAPFL4hf39rOU4/uwTRNhheWUbakgnctqmci9uzs\nxDTFtIK5PXG4h0ha5z0L66nwuKa0t7E5G0wpyC+99FJeeuklNm7ciKrmzeUJUhHY2NjY2JzbZFOf\n2YHdzk/e/e53A9aS9SuuuGKGvTk1hiqmDsh0LlA0z1oUMC83fzx+Yny6SFJu9lGMzSc+6ZgFecIF\niHGJz/N+Tdg10ywKxyr0p+jeUn5aXZIACZHJNW4l95ZzuceRFetaVqwNzrKKpFjXkqIgKyqSqiCp\nCrLisM6qAxwKQpURDtk6KxJCFpiZw5BMTNnEwMCUTXQMuka6IeBg0IgwmAwT18bmtLd2gktIBDyl\nVHrKqfAFqfQGqfSWF539rtMXCFEIwe+fOcKjvz1Ev2miAaR06ip9XLmhicvXNVFdbr0sDSXSPN/a\ny9buYTojlv9uVebChiAXNARZXOFHHiOqD50Y5vfb23lpVxexpBWNfUFTgCvXN3HJmkZKfU527NhR\n9HkG4ike3t/B0ZYeKg4MIyFYubyVi66+hMqGDWhpned+u58tLxwj7VZIXFjOiMeLkvkFuBSJT6xq\nwtnxCMPDrZQE5jBvzUdQHd6cXw++eJRnwyNIEoy2DHL3tUsJVngx02kO/svX0SMR5n3yTvyLFub8\nMg2T3z25n60vtOH2OAivqiZeovDZVXNQJ9AeQgh2betAViRWrGk46ffQPhrnDycGqPG5uHpO9Rv/\nIm1szhBTCvL//u//JhotjvooSRIHDhw4Y07Z2NjMPE8++STpdNrOeXuekV+ybgvy85GvfOUryLLM\nf/zHf+D1esftw968efMMeTZ9ROPa4oqCpdXFFfnL4h3iY9rHqtciMTOBAxOI67yZVDBkXgDnxpGk\n/N5vqdC2YOF4TvgW+zn+moJ7W4o5mUzhcnvILBK32iUwM+q7sN5EQgirzvLRqgMwhYwpiUydwJTA\nFCYi8yxNYYJkBXATCIQkEKaJkAQm1uZ1ExNTMhGygSlbojhbFrKZqRtb1jGlNEIxMKWJ7YRsIOQx\nX4wAxmf9mpgwyIaKM+2hTK/BY/rwCh8lkh+/4qfMUUapsxSv4cKhKbgSKk5TxZlWcSUVRExlxKmR\ncEdwOlVcLgWnW0VR5Dcs0KPxNM9sa+eXzxxhMG7NcLsdClesa+TK9bNYPLscSZKIaTovtA+ytTvE\nkVAUASiSxOqaMi6oD7KyugznmMBj/cNx/rCjg2e3d9A9aP17Hix1887Ns7lywyyaavJZFgr/nmuG\nyW+P9fHU0R58rWEq2mKoqs4FG05wwVXvoyTQzLHDA/z6kd2EhuOklwQZrHVjIlGmpBkxnATcDu5a\nWUPi8I+JRHsIVC9nzoqbkRVr77qmG/zH47vZp+rITpnwniE2l/lYd0EzejzB4a//G7HWVqqvvIKa\nd7w952cinubRn+zg2OFBqmpK8F7SxJHhUa6ZW0NzmXfCZ9zTOUJ/b4QlK+vwlkw+420KwYN7OxDA\nTUub7EBuNucUUwry11577Wz4YWNjY2NzFnA5FIKlbnqG4jPtis0Z4KabbuIHP/gBXV1d3HfffUVt\nkiS9JQR5z7sy+1sL1PJY3VwkwMXE9ePCuE1qV2gjimoyctQKmJbtJwrr82NlA6+JXGC2fIsoKheF\neSu6FhP1Kbh3NBrF6/ViCrPgEFNcm5boHlM/adC8s4hDcuCQVVyZsyo5UFFRpcyBimLVIKOgoKII\nBUWoKCjIpowiVGShIJkyscEUle4aREqxorqnddJJK+p7OqUzImCEKDA+vdhUyLKE06Xicqs4nQpO\nl4rTpeJwWrP7aQRJU5AwTGKawXA8zeGeUYxM0LUaj4P3vWMxV2xqxuVQ0AyTnb1htnaH2DMwip6x\nWxgs4YL6IOtqA/icxf+ZnkjpvLqnm2e2d7CndRAhwOlQuGxNI1dsaGLVgqpcGrSJ2DcwyoP7OuiP\nJmk4NITcncLjSXDZZSHWXPoxNN3NEw+9TstrnSQr3CTe1khUEviI41N0+o1SGv0e7lhWxuDe75NO\nDlPVuJmmJe9BkiyBGxpN8k8/3kaoxoXqcjB6aJiSoSQf/5tLSA8Osv8f/5n4iXYCq1cx987bcy85\nBvoi/Oz+7YQGYyxYWsOKaxbwzV1t1JW4edf8ukk/UzaY21TL1V/tDNEajrGuNsCyqtIpvm0bm7PL\nlII8Fovxox/9iD179iBJEmvWrOHDH/4wbrd7qq42NjY2NucgdZU+DrQNoekmDtWeJTifWL9+Pbfd\ndhv33Xcfn/rUp2banVPi2WMvz7QL5wRSZmZbIrNkXZLAFCh6GFmSCw6p6FqRZRySWlQnSdI4u4n6\njr2WctcT2zlkFafixKU6cSqOzOHMnQvrXYoz36Y6ccjqaVsSnmXHjh2TruoSQqDrJumkJdQtkZ5N\nx2YdqVw5Xx9NpBmJpRmNa4wkrev4qEHCEKQQpMHKOz4BbqAOiUqg2uticH8/Dw+O0udVaDd1UpmX\nIg1+NxfUB9lYH6TC4ywawzQFe1oHefa1Dl7Z3U0ybQV0Wza3givWN3HxqvpJU1hqhklfLEnaMEmY\ncO/2oyiaweKDQ8T6NQJlo7z9T5wsXPMx9rf08dsnXiWS1kluqCZU6kBCsFRqpU8E6TfKWVrp59a5\nKp0t38HQ4tTPfye1c67IfY+HToT45//ZDvP8OH0Okh0Rkp1R7nrXcqS+Tlr+6V/QwmFqr3kncz/+\nZ7kgbof39/HYT3eSTulcdOV8Lnz7Qv7x5YNIwEdWNk86m61rBntf76Kk1MW8hVWT/i5ims4jh7pw\nKTIfWGIHcrM595hSkP/t3/4tNTU13HjjjQgheOWVV/ibv/kbvv71r58N/2xsbGxsTjO1FV72HRti\nYDhOfVXJTLtjcxr5q7/6K775zW/ywgsv5PaTF/JWiLL+zT/5+1y5eBt0sXiTisrShHZj5V6x3cT1\n43ZsjxHFUtamqJzvO5GQnqx/1o2iPicRqScTnDYnR5IkHA4Fh0PBh7W0WdNNhkYSDIQTDOsGg2md\ngZEEg+H8EYlrk47p9zqoKXVTXuLCo8qYcY1kOEFqNIUT8LtUGpqDhIVBp0Owr1zBcJlgmChJHX9v\nAm9fHD8yPRUhXqj0Ul7ho7zCi6bI7D4R4qU9PQyErb3kNUEv713fxBXrm3LbjwCSukFPNElPNEl3\nNElvpjwQTyGAi9PWev+5iorrtW6iI4K62gGufd8iPGWr+dn9OzlysJ/ErBIiC6pIA82lLpamXuHF\n1GKi+LiosYJ3VUc5seuHCGHQvOz9VDZszPnw+20nuO+R3ZQsKcdd7sYT0+k9HGZlVQlLlV72fvFb\nmLrOnI9/jLprr8kFm3zlD60889QBVEXmT29Zy/K1DfxsfycD8RTvmFvN3ICPyTi4t5dkQuPCTfOR\nT7IE/ZeHuommdd63qJ7gmBceNjbnAlMK8sHBQf793/89d3355Zdz663j8wTa2NjY2Lw1yOUiH4rZ\ngvw84+KLL+bOO++kr6+P2267rajtrRJlvc5vB1uyefMYpiAcSTJQIK4HhhNF1+FoatI89G6nQlW5\nh/mNASoDHqoCHioLjqDfRV/nCIf393F4Xx/hTO5wlwRz51RQt7iKdIOPHQMj9ESTAHhUhWVlPubK\nKt6IzoiIE1JcDA/F6O4Ic/xEiBAwhMgtqleARo+DJTV+Zs8KgEvmpdY+oschpBv0xpIMJ8e/NChx\nKMwvL6GuxI2ntQMjrSM9f5xoSmLB/B6uueFyDh1QePZ7zxNzSsQvriPikvGoMjctqEPv+h1PpFaQ\nxsn1C+q4wN3O8d2PIskq81d/hLIqKx2ybpiYvKp/AAAgAElEQVT84Fd7efKlNoKLy3HWeKlzOXn9\nDx24Jbi1tp/DX38E2e1myRf/iuB664WSphk8+fMW9uzswl/m5oMf3UB9U4CjoSjPHO+nxufi3Qsm\njqqeZTrL1U+MxHm+fZC6EjdX2YHcbM5RphTkiUSCRCKBx2OldojH46RSqTPumI2NjY3NmSEX2G3Q\nDux2vnH33Xdz9913c++99/LZz352pt2xsZk2Qgh0Q5DWDNKaQSpzTmtmQbngWi+0M0mlDVpPDPGz\nV15kcCRBaCSZ2789FlWRqQy4WTa3Iie0CwV3VcCDz+MYt1ohHk1x+EAfL25t50j7MElZYLgVRKUT\nz5IAst9JUoHtaR09HYW2KKossa42wAX1QZZXlY5bfm0YJq8fHuCZ7e3s3NuLZpjILoXGmhICZS6E\nKhFTYJ9HYY9Dg7gGBSFA1LRJwICgqlDtcdIU8DG32k9jTSluj4OR4Tj/d0BHTxloaZV16wZYtflP\neOTBNjo7R4gvKiNc70MAG+rK+cCSBl7d+zy/HF0EyHxkxSzmpF+n/cD/oTi8LFjzMXyBWZiGSWgk\nwdcf3Mm+thD1i4OYDT4qXQ56n2/HZ8Lb1V6O/GYPSt0S6t//AXrUSjp3dGLoJjtePUF3R5iG5nI+\n8JH1+EvdpA2TH+05AVhL1ccGsitkZDjOsSMDNM4up7J64hfLphA8sLc9F8htoijtNjbnAlMK8g9+\n8INcc801LF++HCEE+/fv5zOf+czZ8M3GxmYGue6669ixY8dMu2FzBqjLpT6zA7udr3zyk5/kgQce\noLe3l7/8y7+kpaWFxYsX43LZeXfPZ4QQmMLadyyEwDQzwdxMgWFaAeSy17m2omtO0iYQJhgF42q6\nOUZAT3Kt58uF4jpVYJ/WDCbRz28IWUoQLHWzoCkzs13upTLgLhLcZT4X8iTBz9KGSSiRpmMoYp0H\no7T3ReiPJokJE8OlICplqKwo6hcGJE2jVHbQ5PcQ9DhZUVXGmtoAXocy7j5t3SM8taOD7a39JCVQ\nfQ4qNtageFUMrKDygxlbCahwOwmqCiVCwp00kEbT6ANxIgNxIqNJNKArc2wBZNnA49GIxVyAE5cz\nxcqVQ3T3z2PHfa+TqHAzcmENmkvBrQsWhHVcJ3r4ZksHndUVyLrBnPYoW7e9wsvpNKa4EEl28dRv\n9mAYLcSE4EhmD31FpRuz3oucNnC83Etj0gBkuvR6uhoys9xPnQBOFD2DVRuauPaGFaiq9XyeONxN\nXyzFVbOrmV9+8tVbLa91goDVGyafHX+pY4i2kTgb6spZUumf1M7GZqaZUpDfcMMNXHTRRezbtw9J\nkvjSl75ETc1bI0eojY2Njc147NRn5z9///d/j9/vZ+fOnQDs27ePH/3oR3zjG9+YYc+m5ov/bQV1\nG7uUeGzU9JMFCS+MID5dO+seYw0yEdUFxRHPC4KxZ9uz4+Xai+rz12KicceNPX4cAWiahvzL/rxQ\nFgJRILpPh6A906iKhNOh5I6yEqdVVhVcuXoZp6P42lXQx5Vpz/bL2nceP8xlF21AmWRm1TAF4ZTG\nsXCMUDJNKJEmlNQYLihH05PkV/MqqIZMQFWo8XuoCXio8DgpdzsJehwE3VY6sLGzsLpp0hVJ0BNN\n0haKsrcrTG80ieGQkRQJx8IA2ZBsiiRR7XNRX+KmrsRNbYmb+hI3NT73SWeLNc0gPBRncCDK4T1H\nOX60n5ERJ7GYgr8kSkUwzPCwn5aWSnRXjPDyIIlqD5gC//EIpW0RhoXg2MIAseoSlKRO/YEwcjyB\njoaiSvi8pagOFUWV6U5oHByMYAhY2lxGeJ4fCYm1EcHepIYpBCtGjxCsr6Jq00YcbieKIqEoMopq\nHf5SN3MWVOZWIRwLx/hdWz9VXhfvWXTyperCFLRs78DhVFi2emLbSFrnsVwgt5PnJ7exmWkmFeTP\nP/88l112GY888khR/YsvvghYQt3GxsbG5q2H3+vA51bpsQX5ecuxY8d4+OGHczFfbr75Zn7961/P\nsFfTY/fRwamNJmBsLLRpB4QbN1E6xjYbs02ScmWrj5TrWxSwraBNkgqCxE3Qt3Dc7DiSnAkCl+1b\n0C8pGXg9LhQ5EzVdttJx5aKoy1KuTZKsNlnOt+XKBX1zbWNsJAlrrDFtkgSyZN1HVS2h7MiJ6fFC\nuvhaOWlarjeDEIL+PpXOaDIjri2RPZzUctfhpDb+pUsGFXDqAk8kjZTQUdImDhPqK33MagowZ1Y5\nbq+TtGEWHaMpjcF4yro2rbqUYZLUDfpjKfrjqXEvSoRDwmlCk9/N8voADaVe6kvcVHpdqKfwfPR0\nit3bd7Bre4hYzAE4qa4epXluAOSFtB0dJRyNEm3yEVkQwJBgjt/DDfPqaHibB10I7t93nM5QnAqG\nuXNdOanGw8TCJ/AH5zNv9W0oqhvDFPzkqf1s/8NRPC6VOz6wiqcGQ4i0zl3r5vLAfzxDGyqXhnbx\nzrcvYtZNH0SaxjJxzTD50e4TCOC2FbNwTZEj/ETbEMNDcVaub8Q1SYT5xw91EdMM3r+4gYDbDuRm\nc24zqSA/dOgQl1122aRLVm1BbmNjY/PWRJIkait9dPRFEUKc9tRDNjOPqlp/3rPfbTweJ5lMzqRL\n0+aJr+Wjw48T2X/kv9W3WpR1Uwg0U5A2TGK6QSitkdZNkoaRE64pw0TLnNOZclbYaoYgbVp1miny\nZ9NEz1zrpkA3rXqBBAMHx/khAU5FpsSposoSiiQhDGHlKU8baIaJIUskFAkRcEJ5fmtHCNg7Mgp7\nRt/w51cEaJE0qUgaPaZR5XZx0eIa3rG2iWDpm08f3Nt5gpd/v4tDB0x0XUWWFSor0zicfgb6A/QN\nmKT9A+jlLhKba0h4VHwOhRsWN3BhYwWyJBFOavzna0dpH03QJPVwc7NJvHMnyVg/wdo1NC//ALKs\nEo2n+dpPd7DzUD/1lT4+9+H1/PhIF5G0zi3Lmuh4+LccTDgp10e56ZbLaHz75dP+HP97tIeeaJLL\nm6tYVDH10vKWKYK5HQvHeKljiPoSN1fMtgO52Zz7TCrI77jjDsCK2HrttdcWtT300ENn1isbGxsb\nmzNKbYWP1s4RQqNJKso8M+2OzWnmne98J7fddhudnZ18+ctf5oUXXuDmm2+eabemxb3bj07bduwy\n9nz9xBfjl6RPvcZbjDlP2DZ2BrRwyfy4W44facJ7iPGfLx6HX72wP3dPwcRL27NL53NjFtjm+xb3\nKf4845f8F4+XH8ss8LNoCf45hICc+C/CEMgSqA4Ft0PB51bxuBy4FBnnuEPCKVtlSUAyqROPa0Rj\naSLRFCOjSYZHkgwNJ4nG0gjDROiCgN/FFWsbuWJ9E3Pqy970ZzENjQOvv8a2l07Q2elFCBlZBocD\nkii0S17SXifmhgAJj4pZ8A5rc0OQ9y9uwO+yZpW7Iwm+ub2VUDLNYuko11SEiPX3o6cj1DRfSsPC\na5EkmRO9o/zTD7fRMxhj/ZIa/t+Na/ju7uP0x1O8c041NU/+kp8dEghPDR+6Yj6Nb9887c9zYiTO\nb4/1UeFx8qdTLFUHSCV19u/uobzCS/PcinHthYHcblnedEorDmxszjaTCvIDBw6wd+9e7r//fhKJ\nRK5e13Xuu+8+brrpprPioI2NjY3N6acut488bgvy85APfehDrFy5km3btuF0Ovn3f/93li9fPtNu\nTYsDQ5GZduEcRoLoubnSIbusXskt15eQJZAz10p2qXzBkvdsnSKBIsuZs1WvyjKqLOVmtFVZwqFk\nbCQ5N37hffq6u5g3u9kSz4qMmTbo7xih+/gw3cdC6EkdyRC4VIUFCytZtLSG+Yur8ZYUBzuMJzUG\nhhP0DcfpD8XpC0U4kSsniMTTEz4DhypTXe5hXnOQmgofG5fWsHZR9aR72t8I8dE+tr2wlde3J4hE\nvYAPU4ZUwEm81osWdKO588HjJKCx1MO8gI955T4SXce5fNXsXPuhoQj37ThGQjfYILdwgbuLZCSJ\nMFI0LryOmtmXAfDqnh6+8dAOEimD91+5gJvesZjv7WqjNRxjQ00pix/9CS8cGKKz9hIWV3m4+j3T\nF+O6afLD3ccxBdy2ohm3Oj743Vj2t3SjpQ1WbWiacMXMC+2DtI8m2FQfZGHQDuRm89ZgUkHudDoZ\nGhoiEokULVuXJIkvfOELZ8U5GxubmePJJ58knU6/pZZH2kyfbGC3nsEYyyaYZbB565NOp1EUBdM0\n0bTxeYrPVdbXlefKY/9ze8K5LmnitunuIZ9kuDFdx+wPzxhK5A/LVsqPkd0jXtA3Xy8VjS8V1OT3\nqY8fq7urm6bGhvw+8Iy4ze0ZLxCn2TapyC4rkIvrJElCZsz1WBvG12VFs3yObCV4LdzJLIeTw/v7\n2L+/j84Tw7kp+/IKLws31LNgaQ3VDWUMjSbpG47z7K4u+kJx+qctuL0saApQHfRSXe6hJuilOuil\nptxLWcnk0dtPBcPQONKyg5efa6enx4VpyoCXVKmD0Tl+khXu3G/b51BYnBHf88pLmF3mzQnc7N9z\nNlp/z7d2hfjh7hOA4EplGwuVE+hpE0mSmLPiZoJ1azBNwcO/O8RD/3cIl1Ph7g+v56KV9fzsQCev\n942wwO9i3U+/Q+/xDp5p/lMUCf7y4xe9oc/366O9dEWSXNpUOe0o6Lu2tYMEq9Y3jmsbTWk8fqgb\njypzgx3IzeYtxKSCfN68ecybN49NmzaxevXqorbf/va3Z9wxGxsbG5szR12lF7AjrZ+vfPOb3+Tl\nl1/OvVD78pe/zNVXX82dd945w55NzZ1r5sy0C+csO0a6WTevdqbdmFGEECTiGpHRJJGRJNHRJJHR\nFCPDcQ7s6ScR68HIpOPy15Tgq/Ahex1EUzq/aRvkf3Z0nDOCeyIS8TQHdreytaWNnl6BHDWRTQ+m\nLBFv8BJp8qH7HNT73MwPljC33Me8gI8an2vKGAtCCJ5u7ePxw5ZovcaxnWqtFWGCrLiYt/o2SisW\nEE9qfOOhnWzZ20t10MvffHQjc+rL+F1bH88cH6DWKbP5x/eR7O/jtw1Xk1Bc3HDpvNyL3unQPhrn\n6dZegm4HNyyennge7I/ScXyYuQsrKSv3jmt/7FA3cd3gxqWNlLkmDvZmY3MuMmXas+rqar761a8y\nPDwMWG/ct27dyjve8Y4z7pyNjY2NzZkhN0NuC/Lzkq1bt/Lwww8jZyIc67rOhz70obeEILf548Mw\nTJJpndFImlAoznA4zshIkvBIkkgkRSSaJhpPEY9rxBM6hhAYCEwoOjRAVyRSRmZavC9iHRmcqkx1\n0MuCWQFqyr1UzYDgnuizHz02yPYjfRweCDOaNHGH0ngGDVTAcMpEmkqoXlzF2qZy5gd8zA74Jsxt\nPhU/2dvOix1DlLsdvNe3G+fIEQBUp58Faz+Gt7SB7oEoX/7hNjr6IqycX8ndH95Aqc/Jjp5hfnGg\ni1JJcOlP/ws5HOLo4qs5oNUS9Dm56Zol0/ZDNwU/2n0CQ8CtK5rxTPOztGzPBHPbMGtc29HhKC93\nDtHo9/C2WVXT9sXG5lxgSkH+hS98gUsvvZQ//OEPfOhDH+KZZ57hq1/96tnwzcbGxsbmDFFR5kFV\nZHuG/DzFNM2cGAcr6vofe4TyP2bMglzlhimKrieqK7zOlbP1QmAaAkMIdN0klTZIpnVSmkEqbUx4\njic04kmNRFInmdJJpQ3SuhXdXDMmC833xlEV62VjVmDnzx6qg14CJVPPIp9pTCE42DHMjtZ+jg5F\nGcBEcyt4BhL422MER63tJXKpyoILZnHhBbNpCHjf1LYAUwgSJrzYMcSsUg83VLQT79gFgNNTycJ1\nH8flDbLjYB9f+8lrxJI67750Ln923TIUReZIKMr3W47jECaXPXI/JYkY6vv+jN/s0kCC/3fjGpzT\nFNWaYfLTve10jCa4qLGC5VWl0/sMhsnu1zpxexwsWlG8SsQwBQ/stcT6LcuazlhqPRubM8WUglxR\nFO644w5efPFFbrnlFm644QY+97nPceGFF54N/2xsbGxszgCKLFET9NIzGJ9pV2zOAMuXL+cTn/hE\n7m/1K6+88pYJ6vZvD2bi1hRGRy+KlD5x2PRCUTdplPNJxpks2LoQIh9lPBupXBS0ZcYsKjNFn4n6\nF0Y7FwWR08f0j8XjOH/3TF5MC4FhTCCux1yfK0hg7VXPHArgAByKlc/c5VTwuFQ8bhWf14nP66DE\n56LU78Jf4sLtUnA71Zyty6nkyw6Fg/t3s379+pn8iONIaAaH+kfY2TZI63CMQQzMTJA3yQklnXGq\nO6LIaet7mr0wwKVXLqF5XsWbenkQ1wx29g6zpTtEVdIS+curSnl/3Sg9+38PgNffwIJ1H0dxeHnk\n2SP8+Kn9qIrMX9y0hivWW7PQvdEk9+1oxTBMLn/qYerQmfXFv+WrDx4lCqxbWMX6pdPbRjEQT/Ht\nncesFGulHj7wBvZ5tx4eIDKaZP2FzTjGiP/n2gfojCS4sCHI/GDJtMe0sTlXmFKQp1Ipent7kSSJ\njo4O6uvr6erqOhu+2djY2NicQeoqfXQNRIklNHwee7/d+UJHRwf33HMPTz/9NC0tLUiSxPr167n9\n9ttn2rVp8dyOzpl24YyRD+w2JkhcYX0uqNv4NtMwcRkaspwJrKbIOB1SUZA3qXCMwv4UB6DLqnwp\nI/zJvSwQYGZeEJjCqjczhxAIwxL6wrTsCgX22KPE66DU76aszE2gzENZwIO/zEWJ342/zI2/1I2v\nxIl8GqKQW4/o7M2Mpg2TmKYT1wximk4sbZ0jSY1wPE0omuT4SIKwMPJBBRVwxE2CkWHK+iPoQzKm\noaCqEqs2z2LTZfOoqDp1QambJvsGRtnSHaKlbwQt8zKmXpZQENw2T6b1tYcBKAnMYf7a29FMmW/8\ndAcv7OqioszNPR/ZyMJZVmDF0ZTGvVsPE9MMLnr+SRa5ZRb/w7/ws0cO0aYZqLLEJ9+/elJ/CtnV\nF+aHLSeI6waXNFVw49ImnG/ge9+VyT2+asxy9ZGUxhOHu/GqCu+b5l50G5tzjSkF+e23386rr77K\nxz72Ma6//noUReG66647G77Z2NjMINddd11RhgWb84/aCisoTs9QjPmNgRn2xuZ08Oqrr/L5z3+e\np59+mmuvvZZrr72WQ4cOcdddd7Fp06a3xCz5X79vJWDpRQmpKOe1sKaMkcjPJiNJmVlq8lbZapHP\nj51ty0pSIUDKto8ZP2ctBMK07ibMTCZvUyCEZM1cZwSrmZnezolYLNGKmZ8JFxmRa2aFbWYWO9un\nsD5nN8Y2Fo3jcDjRNANdM9ATGrpmTCed+ikjZfJ0OxwKqkNGdSioqozqkPF4nfhL3ZRkxLW/1JUX\n2n7XaUn3dSYRQpDQC4R1WiemZ86aQVzTiaYtkR1J6cTSOnHdIGmaGNMYXzJMXKMaQcOgVg5RnjrO\n6GAJff2VpHFQ4new8ZJ5rNvcjMfrPOXPcCwcY0tXiNd6holqlme1PhebGiq4oL6cSu9admz9Hcd2\nfBcQlJTPZeG6O+gPp/jnH27jWPcIS2YH+euPbKDc7wYgpRt885UDDKV0Vr32ApuCHhb8xV+xd08/\nLxzuRwNufvtCaoLjg6sVYpiCXx7u5jfH+nDKEh9Z2cxFjW8ss0c8muLQvl6qa/3UNxXncn/kYBcJ\n3eTmZU2U2oHcbN6iTCnIr7rqqlx527ZtxGIxysrKTtLDxsbGxuatQD4XuS3Izxe+9a1vcf/99+P3\n51MILVq0iG9/+9v867/+K9///vdn0Lvp8ftH9860C+cG2bRlcialmSwhhAnCQHXIuN1qgThWioSy\nw6GgqgqKKueFdNZOVXI2yhh7yy5/djjk0zZ7fbrJ7m83MmddCMI6tA5HiWVmrXMiOzuLXTibndZJ\n6AbmdG8oBLIukDQTWTdxaCZypizrArcs4VUVfA4Vv8tBiUtQ5+nHXbWLvh6VtmMNHBmxMgjUNpSy\n+bJ5LF1Vj6Ke2vPtiyXZ2hViS/cwA/EUAKVOlatmV7OpIcisUg+SJJFKDNN7fCuM/B6Bia9sFgvX\nf4K9rUP8y4+3MxpL887Ns7njPStwZHwxTMF9L+ymPSmYf7CF6+ZU0XzLTUQjaR57bA99QHW5h/dd\nvuCkPoaTGt/b1cbhUJRqr4tPrp1DY+nJBfxE7Hm9C9MQrNpYnHv8cCjClq4Qs0o9XDar8g2Pa2Nz\nrjClID906BCPPfYYkUikaE/WV77ylTPqmI2NjY3NmaW2Mp+L3Ob8QAjBwoULx9UvWLCAVCo1Ax69\ncd75nuX5HN7ZXNzZXOMFy7izSEU2BeUxy8Pz+cql4raisYrHlwoEsSRJuQjckpzP1Z1ZJ44sk7GV\n8kvFZUtIk63LjpXrk/ctZ5e9h5RbB5CbtN+1q4WVq1blZ90L9psLBNnt4kJY5WxvU+RtcvaZviaQ\nzM7KAwgdkQIzmV8xYE5wLyEsUaxnRLFu5gWybpoT1BWfDXOMXa5u/HhZwW2YZq488dZ4CV49fJJf\nF9Yy+4yQVnOiOlOXFdiaiQsJn1OhxOWgzOOkzOegxO/GV+HCV+LC65VxOJKoShRJhEknh0knQqRy\nh0b7iTpOtC8kkXQBsGhZDZsum8esucFTWl4fSWls77H2hbeFrfgfTkXmgvpyNjVUsKTCjyJLJGP9\n9LZtIdy/h/hofguIy1vFwvWf5MmX2vj+r/YiS3DXDau4ZvPsnI0Qgh/83xYOmC7qO9u4dWUz9Vdd\ngRCC//1FC0dS1n70T/zpypMGcjs0FOG7r7cxmtZZVxvgtjcQTX0sLds6kGWJlWvzucd1U/BgNpDb\n8llvKuidjc1MM6Ug/8xnPsN1113H/Pnzz4Y/NjY2NjZnidpgNhe5HdjtfCEen/y7DIfDZ9GTU+eH\nsen7OflS7YkbJjM/2ZLvcyckGoAEv989006cOYS1J10S2UNYZzP3VgDJFDhNay87ZkF7Zga7UFQX\nlhUD/E6FEq8LX4mTEr8Lb8nYsosSvxNviQtVgXRyhFQilBHavXnBHR4i1B8FIJ1WicU9xGIeYnEv\nsXiAeKKRSMSFaUo4HAobLmpi4yVzTml/eNowaekLs6U7xL6BUYzMlo1llaVsaihndU0AlyKTiPbQ\nd+xVhvv2kIz1ZXpLSLKKMHVQgsxZcyf/+Ys9/H57OwG/i7/68AaWzc0vHxemyYOP/IbtJbUEhwe4\na9MialZa21z27Ohk64E+IsDGZbVsmCSQmykEv2nt45eHu5El+OCSRq6cXXXK+/t7Okfo7R5l0fJa\nfH5Xrv4PJ/rpiia5pKmCuYHp5z+3sTkXmVKQNzQ08OlPf/ps+GJjY2NjcxapKViybnN+sGDBAh56\n6CFuuummovrvfe97rFq1aoa8emP4UvlFxIVC+bTPf53KxutJFT1IkzVOVD156PeT2hi6gaIouSBs\nuaZciHbywdnyU+GZsmUsCuwLy0WfIb+p3qoruIZMILjsfv7M1LokBJJJRiRbQjkrmHN1OVE9pl+2\nPOYxKaq11F5RZVRFzl9nyvl2FVWViekjNM9pwJcR2j6/K1f2eJ251QfWYxLo6UhGcA9ZQjsSoq8/\nRCo+RDo1AsL6LRqGTCzuJhbzEot7SaTmEI/7iEQdpJLjf5kOp0J1rY9laxpYu2nWG94fbgrBoaEo\nW7qG2NkXJqlbfswq9bCpIcjG+iClToXYSAeDx14l3LeXVGLI+m5kldLKpciyQnhgH8I0qJ17JYe7\nA3zp+7s43B5mflOAL35kI5UBT+6eRjLJEz/8Gc81r6AkEeUvLl5KTXMTAJHRJE8+vpcOBA5V5uPX\nTxyLIpbW+UHLcfYMjFLudnDHmjnML39zUc/zucebcnXhZJpfHenB51B47yI7kJvNW58pBfn111/P\nf/3Xf7FmzRpUNW++YcOGM+qYjY2Njc2ZxeVQqChz02ML8vOGL3zhC3zqU5/iiSeeYPny5Zimyc6d\nOykpKeE73/nOTLs3LUpf6J5pF04/BZHOc0voc/X5xqKl9QUzitml9KZpZHLKFyyPH7PX3KqTTmpD\nxkYutJEL+srF48gF4xTVK3nBrBSUi86KkrcZI6jH2SsyimrZy4r0hmdVd+zYwbp1i3PXhpawhHYs\nRGQwRCpRvKxcmFrO1jQhkXQTi3lIpsuJJ2cTi3mIRlSi0Qm+UlmiPOhl1hwfwaoSKqp8VGTO/lJ3\nkfifLp2jcbZ0h9jaPUw4k6os6HZyRXM5F9QHqfM5iYaPM3zsVU7070VLjQAgKy7Ka1dRXr0Ct7+e\n9v2PMjrYiuwow6y4nhfbnDzx3GGiSZMr1jdx1w2rcBUsH08NDfG7/7qf365+G05d47MXLaW21tqT\nLYTg17/YzbFk2grkduVCaivGz0gfD8f49uttDCXSLK30c/uq2fjfZJA1XTfYs7MTX4mT+Uuqc/W/\nONBFUje5dfks/M4ppYyNzTnPlL/iX/3qV7S1tfHSSy/l6iRJ4oEHHjijjtnY2MwsTz75JOl0mnXr\n1s20KzZnkNoKH/vbhtB0A4d6avv7bM4dqqqq+PnPf86rr77KkSNHUBSFa6655i31Ev3z//iOKW2m\nI9TGm4zvMx29lxWwuRGyIjpXzhsWCe0zsKfVEpx/XP8mC9PANDVMI41ppDGMNKahZY40pmnVm3oa\nYodobdlvie74EIaeKB5LQCrtIJkMkNTmEU/4icU8RCIKI2EDc4IIb/4yN7PnW2I7WCC6A0HvaYki\nH0qk2dYdYkt3iK5IEgCPqnBJUwWbGoLMLXMTC7USbn+F3f370DXrBaqieqioX0+gegWlFQuQFQfD\nfXt56dnvcaTXTUd0I8cGPMRTx3jv6hRvXwwVzet49yVzi36b0dZjvPwf3+Gpt10PssSn1y+kuaY8\n175nZxe79vfSj7XN6X2XF29hFULwfPsgPzvQiWEK3jW/lusW1J2WPd2H9/WRiGtsftu83LM+MBhh\nW88wc8q8XNz0xqK129icq0wpyEOhEIV9MQYAACAASURBVM8888zZ8MXGxsbG5ixTV+Fj37Eh+kJx\nGqv9U3eweUuwefNmNm/ePNNunBKnmv7J5uxjpYWzxLGREcxmTjBnxbJWUJ/OiOvp2FtnIaaTYCxP\nOAm64UIzakikyoknSojGXERGZMLDOun0eNXtckvUNgbys9yV+Vlvp+v0z8AmNIOdvda+8ENDEQSg\nSBKra8rY3BBkWdBDfPgI4a6X2bvrAIZuCXXVWUJl4ybKa1bgL5+HJCuERpM8t7OLV3bsYn97mkhq\nWe4+dZVuLltbRZl8HITB9ZfOK/JjaOs2dv3X93j62pvRXG4+trKZJQViPDKa5OnH9tBhZR/kjveu\nKArkltQNfrq3na3dw5Q4FG5fN4dlVaWn7Tllc49nl6vrpslD+zuQgJuXNdmB3GzOG6b8V2bDhg20\nt7cza9asU7rBV77yFVpaWpAkiXvuuYcVK1bk2l555RW+8Y1voCgKl156KXfddRfxeJy7776bkZER\nNE3jU5/6FBdffPEp3dvGxsbG5uTUVuYDu9mC3OZcYLgvE7Rssn3WJ7s6SZ/ilqJk4yfxJrPXOrvJ\nOrsPO9dWkME8u696XL+8rSgYY/J+hePn2wQC4t10Hu4FYWbyk5tWGesaYRTUC0Tm2rKxrsm05/pm\n27N1jG/Pl83MZ8iUTf0kz+4NIsnIihNFcSJJDiTFjyQ7MU3rMEyHdRgODENB1xV0Q8bQZXRdRtMk\n+vvipJIqsag2ZnAdRZUzQjs/y50V3V6f84ysaCjywBTsGxhla3eIXX1htEyY+PnlPjY1BFlT5UUP\nH2a492X27z2ImVlO73QHqGjYQHn1CnyBZhIpg33Hhtj10n52HR6gvTeSfYD4nAoXLguyduksVi+s\nyuUIf/LJDtLp/IsNIQTdv/wVhx/8Gf93/YeJlZTx3oX1bCrIDy6E4NeP7KY7qTGKYOPS4kBuPdEE\n/72zjZ5okrkBH3eumUPQc/pepo2OJGg91E/9rABVtdbfpmeOD9ATTXLZrEpm24HcbM4jphTkL7/8\nMg888ACBQABVVRFCIEkSzz333JSDb9++nRMnTvDwww/T2trKF7/4RR5++OFc+z/90z9x//33U11d\nza233so73vEOtmzZwty5c/mLv/gL+vv7ue2223j66aff1Ie0sbGxsZmYbC5yO/WZzbnCsZafzLQL\n5zR9xw+cppEkJEkGSUaSZEuQ5sqZeiRkWZnURpJVkLKC2YlpqpimE91QMQwFw1DRddkSz7qEpkvo\nOmga6BpoaYGmmaTTJlraIJ3SSaV0dG2q7OACmPhlQCDoYN6iwLgl5qUBTy5t3dlCCMGxcJyt3SG2\n9wwTTVs+1/hcbGoIsr7KjRo9QrjvZY4eOpxbDeDyVlFes4JAzQqc3jqOdIR5adsAu468zKETwxgZ\nMe9UYX5lmDnBYdYtbeKCTdehOlyT+gNgahrHvvN9ep55luff9SFCwWoubarkmnk1RXZ7X+/iwL5e\nuhUJhyTx8ffkA7lt7Q7xkz3tpAyTK2dXccPiBlT59Oar3/1aJ0LAmo3W7HgokeZ/j/RQ4lR578L6\n03ovG5uZZkpB/maCwLz66qtcddVVAMybN4/R0VFisRg+n4+Ojg4CgQA1NdY/AJdeeilbtmyhvLyc\nQ4cOATAyMkIwGDzl+9vY2NjYnJxaO9K6zTmGp+LagivpJBPgUpHZWIMJJ8ulgljoYhJxJsZfCmHt\nDxeZm+Un1aVcRHMh5ccsDIBu/U+2j3V/iXwA9MLPKcb1EUX36+3to7q61soBbkqZ2WsJ05oQt84m\nVt7x3FkUtFtl0xRWvRCYhsjUm/n6wsMwM33NAlvr0DQDQ59IPOuZIzXxMy5AkiVcLhWnU8HjdVJW\n7sHhVHG6VKvepWSuFZyZ+ny5uO5I6wE2blw/5T3PBEndIJzUGE5qhFNpeqMpXusZpj9uPQO/U+XK\n2VWsq3RSmjjKyMBLdLUdy0Vy9/jrKK9eQVn1cgaiPnYcHaDlhS72tLaQSFlCXpZgflOAFfPKqVX3\nUE4LLqeb5mXvp7xmxaS+ZdGjUQ7+y9cI79nLtutupKu2iZXVpdy8rKl4X/lokt88vpdeWSJpmNx8\n9SJqK3xohsnPD3TyXPsgLkXmzjVzWF9XfpI7nhpCCHZt60BVZZattqKo//xAJynD5MaljfjsQG42\n5xlT/qK/9rWvce+9957S4IODgyxfnn+jVl5ezuDgID6fj8HBwSKxHQwG6ejo4JZbbuGxxx7j6quv\nZnR0lO9+97undG8bGxsbm6mpq8zMkNuC3OYc4ZEHI1Mb/RGzjzMXhV6WJevIRDi3ynKuXlFlHJmy\nJFs5tp0ZMW2JYhVHtpwVzBlh7SiwydsrKIp82paLK8dP/wy4KQSRtJ4R2+m86E6mCafy5cQELyac\nssTG+nLWVTio1VsZHXiB0c4TjGbafWWzCFSvwPQs4GCnwW+2DtBypIXQaP5FRkOVj1ULGlm9sIoV\n8yoRqU7a9jyElgxTUj6XOStuwukOTOODmLR8/q9Jdndz6PobOVg7j+YyL3esnoMyJh3crx/dQyie\npkcS1AS9/OkVCxiMp/jO620cH4nTUOLmE2vnUlvifrOPd0I6jg8TGoyxfE0Dbo+DfQOj7OgNMy/g\n48JGO5CbzfnHlIK8sbGRRx55hDVr1uB05veGNDU1naTXxIiT7NPKtv3qV7+ivr6e73//+xw8eJAv\nfvGLPProo1OOvWPHjjfszx8T9vOZHPvZTExdXR1gP5+pOB+ej9sh0dY5dNo/y/nwbGzOPhsvmZMr\nS+MKxRcT6bjJxJ00wRhFdRNMuOfGK0hPlk35le2fvV/+XNiHTIqxgv6QS4mVTWdWPE6mTi7on3Hq\neNtx5i+YiyzLSFnxXHhkxLMkWaI63yaPscuXpUx7oQ9/LKQNk3AynRHVWkZgF4vukZSGcZIwA16H\nQtDtJOB2EnA7KHc7KHPKlEgJKtPHiA8+T+JAFz0ASJSUz8MdWEZntI4dJxLsen6Ajr7XcuOVlTi5\ndE0DaxZWsXJBFdXl1j5wYRr0HHuGnmO/B6B+3tXUzr3S2l4wAUYqRby9g1hbG0s6e+h97nmSiQQD\nN3+ELf4GKj1O/nz9PFxjsmvse72bg3t76HOriKTGHe9dwaHhKD9oOU5cM9jcEOSW5bNwnYYI85PR\nkg3mtrEJzbADudmc/0wpyJ966qlxdZIkTSvyenV1NYODg7nr/v5+qqqqcm0DAwO5tr6+Pqqrq9m5\ncyeXXHIJAIsXL6a/vz+3b/1k/LGlAXkj/DGmSZku9rM5OfbzOTnny/NpfCHCid4Ia9asPW17LM+X\nZ3OmsF9WTM47C/aq2hSTFn0sWWnvn50KIQTRtM5wKiO0J5jdHk5pxLXJI7grEpS5HDSX+QqEtoJf\n0SmRU3hFHK+IIOmjpJOjaKlRtMgo2uBoLt3aECBJCr7gYkL6QlqHytizZYRD7cOYZggAl1Nh3eJq\nVi+sYtWCKpprS8f9O5xODNO25yGi4Tac7gBzVtxMSXn+xVU6HCbWdjxztBE7dpxEdzdFedxUFXHX\nn/O06cenKnxmw3xKx+QJj0ZSPP34HkYVmYGkxoYlNXQpJk+91ooqS3x4xSwubqw4oy9u0imdfS1d\nlJV7mDO/kqeO9dEXS3F5cxWzyrxn7L42NjPJlIL82WefPeXBL7roIr71rW/xgQ98gH379lFTU4PX\na/2fqaGhgVgsRnd3N9XV1Tz33HP827/9G+l0ml27dvH2t7+drq4ufD7fH90bWxsbG5uzSW2Fj6Od\nI4RGk1QGPDPtjo2NzXmOEAJDgGaYaKaJZoqJy4YoPpsmaUOgm+Pb04ZJdwh+8Ye9jKQ0dHPyaW2P\nKhNwO2ku9RJwOwi4VEpVgxI5jU9K4DGjuIxRjPQoWmoELTlKemQUQ4sDYACRzFGI4vCiuMoQrllE\n9TI6ItUc7lHZ/4dhEqkQEEKWYMGsclYvrGL1gioWNQdxqJPPNg/37ebEvkcw9ASBqhVUl19EYm83\ng20v5wS4Nhwu9sPjoXTxInxzZmeOObzUN8iTUReyJPjUunnjlpsLIXjq0d1E42l63AoOSUae4+ep\n1l6qvE4+sWbuWRHEB3b3kE4ZbLqsiVAqzVNHe/A7Vd6zsO6M39vGZqaYUpD39/dz7733smfPHiRJ\nYvXq1Xz2s5+dVrC1NWvWsGzZMm688UYUReFLX/oSjz/+OH6/n6uuuoq/+7u/43Of+xwA1113Hc3N\nzXzwgx/knnvu4dZbb8UwDP7hH/7hzX9KGxsbG5tJKdxHbgtym3OFsdvcTpqcbIJAbJNenWSgsU0i\n06GwXmSzkRUmTysKyDbGPndPMea68H6iYNxCP0WR/bAOXZGEFWQt08cUwgreRuYsCs4UXGMFccv1\nKWwbN5513yL7sffASuWlmyZpYxIhnavLCud83cm+z1NFAspkQaPfkxHaDkodJiWyRomUxCtieMUo\nsjZCOjWKloqgDY+gp2O5byOdObLfQcpQSJk+0qKSpOknaXpJ6h4SmpOYphJLykSTEEkYjMY0ool0\nwW/MEsqN1SWsXlDFqsw+cJ+neGZ6IrTYKG0vPsjw/tcRQxqOmI/+7t/Qm3qiyM5ZWUn5hvVF4ttd\nU41UEPX8eDjG00fDpE2TO9bMYUGwZNz99u3q5uCeXiJlbiIjCSrmBziRSrG6poyPrmzG6zg7gdR2\nbbeWq69a38RD+ztJm4JbFjectfvb2MwEU/66v/SlL3HJJZfw0Y9+FCEEr7zyCvfccw/f/va3p3WD\nrODOsmjRolx5/fr1RWnQALxe7ykHkbOxsbGxeePkIq0Pxlgxr3KGvbH5Y+fjT+2caRfOYSR48XSl\nPTuzSIBDsYLAOWQZhyLhVR25ctE5U1YlUGVQEThkgSKZqAhUyUTFQJFMFAxUDGShW2d0VDRkU2Ok\nt43yMqe1fDw6ijYUofA1R9SU6E87iKcdxDSVpO4hadaSNHwkdJclstMqsZRENCGIJAz0CTeQm0Ay\ndyVL4Pc5CfhdzKr1U+pzUlbiYnFzOasWVJ30RacQAm04bC01zyw7j7QeIdXTX3xHJYqnqRHf7Nn4\n5lrC2zd7No5S/6RjnxiJ879HemjpHwEk3r+4YcKo6NFIiqcf20NalTg6mkDxKDibfLxvcQNXz6k+\naytVQ4MxTrQOMXt+BZ2Gxut9I8wv97G5wc64ZHN+M6UgTyQS3HLLLbnrhQsXvqll7DY2NjY25xa5\nXOR2pHWbc4CFBbN342SANPZSmqyp2O4kjdKYnhMGihvTlu1TaCtRHLitcLycfYFxoX3RPSgOriYV\n3Gdw4P+zd+fRcVV3vui/55yaqzRVabQsy7Zsy7ORZUaHIUA76UQEQmxDSNLp5HboJu+mOyS9OtNq\nsm7fcMl6HW7gdaDT9O10k9UQ04YQQCHkBhKGgA1GnmRbHmXJsjWW5prrnLPfHzWoSipNlkpHlr6f\ntYo6c/1cC7vqW3ufvb0oLiqEJAFy8toCcvJ6sWUAkCGS58rx9vvYOfHtyWMSj8Q+kX58cr+efA0p\nvl8RGhSo8YcGWY/EwrGIAroKiCh0TYXQVeh6FLoehdBU6LoKXY3GtmtR6HrsmER41gWg6TI0XYIm\nJGi6jLAee06sa6PWVV1CKGpCIGpGIGpFSFuGoGqLhe+wAl8YCEcz/i8wigqHzYQ8pxXFbgtynJZk\nwM6NL+c6LchzWpHrii07beYpjb8hNA3BS+2x4N3SAn9zLIRHBwfTD7TIkJbY4FyxHCVX3QLXqio4\nKiogmydvWQdGB3FgVYETa4UPO1aWjDk20VU9EIigNccMMayhdH0hvnHD2rS/i3PhSLx1fOPVsdZx\nWQI+N2pKNqKFaEqBvLu7G8XFxQCAzs5ORCKRSc4ioitdfX09IpEIB+ZaBEbmIg8YXAkRcHMgpUuu\nEPGomNyQ2DyGGG/HqKPGO0RAGnO+SP2vnqhFpL1W+pzjKZ3ZRTy2Jq8ZmzM80aU9tWN78hqpf95k\n1/fEJOeAG4Doi82LHuviPvZZTXSfn+CYtOfE3OmJc6Zxblo4niAox9Zt0HQHNKFAT+wXsePV5HkS\nNB3Qx5sj/jKYTTLynBaUF6UEapcFuc5RATseuHMclgnv6Z4KIQS0QCA+ynlLsvU70HoB+qjv0Nbi\nIrivvRr2ZeXwyRcRtHTB7M7D8k33IL9o/bReN1MQ/9TqMpzb/3b8u/vYz/MTRzpwsrETvU4TBoYj\ncJe58P9+pnbMgG/ZpusCRz5sg9VmQptLRk93GLcvL8bSXA7kRgvfpIH8q1/9Ku6++24UFRVBCIG+\nvj48/PDDc1EbERHNAU+eDWaTzBZymhf+56/XGF0CZYksSzAp8e7pJhlmkwyLSYZJiT3MpvTntGVT\n4lw5ee7oY3q6LmLzhmrkpQRum0WZsIVVCAERjUILBqEFBxHpCyEYCkELBqGHQtCCIWjxdS2+rqeu\nJ5YT2+OPtBHOAUgmExwVFbH7vJNdzithcrkw3Hc2Nrd4eAh57nVYvvFeWGx5U35fRwfxqngQX+fJ\ngSRJODfOeb6hEF5+/ghUCWiNaFAUCf/ri9fOeRgHgPNnejA0EEL19RX4bUs38qwmfGo1B3KjxWHc\nQH78+HFs2LABHo8Hr7/+OlpaWgAAK1asgNVqnav6iIgoy2RZQonbgU4vAzkZ7/pNU/sSnrlr+QT9\nzSfeFL/m2D1S8j+x6ycPSemantbFPNOxKcdk3Jb2GuNslyR0d3WjtLQk1g09Pr95Yt7xkW1SrAv7\n6G3y2G2JucflUc/p2+Jzoo/aBgkwybGgbFaUZGBOBOTUwGyKLyuyFOsxoOsQug6habGHrkNosXXo\nWso+PWW/Nuo8Ne1c6BrODTejvCUALRhCOBRCZ1poTgnZyaAdC9Kjw/N0yTYblPjDnJcLxW6HYrfB\nXl6eHGjNvrR8TJdzoWu4dOY36Dz/B0CSUL76EyhZfvO4c4uPdmEwgFfOduBwV+YgPpGwpuOf/+N9\nRIMqLrgt0PtCuPdPqlFeNLfd1BMOx+cev1BiQ3Q4gF1rl8JuViY5i2hhGDeQf/vb38bjjz+OH/zg\nB/jRj34EpzPWpbG7OzbIREVFxdxUSEREWVfqceJitw++QAQuh8XocmgRuzfaOPEBUxqee/KDRo/i\nPvFlUo4d1VU9bX/qxrRTRIaN6ddL3Zi+aWSlt7cXbl9B7Lj4sOxCJJbjHeGTw7ILCD0+9nvqNoFk\nAE2eG3+d9GulX1skh4FPrMeuo+kaVC09RCOxrMdDdUr4nmn4nUzzJPtliwWK3QbZZoO1qCgZpBMh\nWh61rthskG3xZbsNis2e3C/bbFCsFkjK9INjONCH843PwD94ARa7Gys3fQ7O/GVTOvfCUKxFPBnE\n8+NBvHDyIA4Anb4QfvrKUcitQwjmWTAwEEax24Gdt62e9p9jNgQDEZw81glzVT5ODwdQ7XbhmiVj\nB58jWqjGDeQf+chH8Jd/+Zfo6urCF7/4xbR9kiThjTfeyHpxREQ0N1KnPlvNQE4pVFXFt7/9bbS3\nt0NRFDzyyCNYunRp2jEvv/wyfv7zn0NRFOzatQs7d+6Epmn43ve+hwsXLkDXdfzd3/0dtm7dOunr\nddS/mq0/yoLgnasXijWfxwJestlfGgl8kgRJUSDJMiRFBmRlZN1sii3H12P75JTjM5ynyCn7YtsQ\nPyb1WoljIKdsj5/b1tmFldXVySCdDM32kRbsywnPs62v4zBam16ArobgLq3BsvV3QzHZJj1vpkEc\nAD7s6MfPG5pRcKQHkAE1zw5tMIT779wIq0Et0scOtSOq6xhc4YICgfs4kBstMuMG8m9961v41re+\nhcceewxf//rX57ImIiKaY6We2MA5nd4AVlewZYJG1NfXIy8vDz/60Y/w7rvv4tFHH8WPf/zj5P5g\nMIgnn3wSL7zwAkwmE3bu3IkdO3bg9ddfh8PhwLPPPouzZ8/iO9/5Dvbu3Tvp61312KOTFzWl7+pT\nOGgKX/pHDpHGbkw7PdElXRq9adSFxnZvT69j7LUT12xsbMSmzVuSfdmlWP/z+HLiOvGR3iU5ZYj2\nlP3jhezUfVeojoYGFM3jgUg1NYy2ky+ht/0AZMWC5RvvgbusdtL3/MJQAPVnOnAoHsRXxoP4+mkE\ncQFgz4k2vNHSg6IT/VCiOpbWluPFhjZsW1eCazaUzvSPd9mOHLiA4RW58AmBj60sxpKc8aeJI1qI\nJh3U7YEHHsDrr7+OwcHBtO5dO3fuzGphRGSsuro6NDQ0GF0GzRFOfUbj2bdvH+666y4AwA033IDv\nfve7afuPHDmCzZs3J29t27p1Kw4ePIg777wTdXV1AAC3243B0VM7jcO5Yvms1b7QSHl5sBZ6jC6D\nLkNg6CKajz6LcKAHjtylWLHpPticRROe0xZvEb/cIB7VdHT4Q3BfdT1ebWpBV0sPlgypULqCKFuW\nj7fP9sBsknH/XZsM+yGmq2MIrV4fhq8vQb7NjLpVHMiNFp9JA/lf/MVfQJIklJeXp21nICciWjhG\npj5jIKd0Xq8XbrcbQHxAL1mGqqowmUxj9gOx8N3T0wNFUaDEuwc//fTTyXBOtJgIoaP7wh9x6fSr\nEEJDSeXNWLL645Dl8b+Ctw0FUH+mEwe7BgAAK/Id+NTqJdgwQRAfCkdxcSiItuFg/DmATl8IWrIt\nTUKtOwe+/c2ImGSoZTnovdCHe/5kTfKWJSMc+uACBtbkQ0gSdq9bCpvJ+FsKiObapIE8Go1iz549\nc1ELEREZpNTjgCSxhXyx27t3L55//vnkl34hBI4ePZp2jD7JoFyjB0t75plncOLECfz0pz+d3WKJ\n5rlo2IeW489hyHsSJosLyzfei7zC6nGPvzgUwCtjgngZNhTmJv9OqrpAlz+EtqEgLg4HYs9DQQxF\n1LRrWRUZlXlOLM2xY2muHcGOCwicGUK3P4Kaj67Ev711DsUFduy81ZiB3ABA03TsO9+D0Jo8rHW7\nsK0037BaiIw0aSBftWoV+vv7UVDAewqJiBYqs0mBJ8/Oqc8WuV27dmHXrl1p277zne/A6/Wiuroa\nqhr70p9oHQeA4uJi9PT0JNe7urpQU1MDIBbw33zzTTz55JPJ1vLJ8FaZifH9mdi8eX8iHYDvfUCE\nAHMpVOd1ONvqA1rH1tcbBRr8QHM4FrqLTQLbXECRyY/ms2dxoAnoVWOPPhXQR42P4JIFKi2Axwx4\nTLFHnqJBknxAxAd4AX9HECcO9yPPY8LvTrRB0wVu3eTA8cYjc/J2ZHKxLYiuCickIXCVNIyDBw8a\nVgswj/7fmaf4/mTPpIG8s7MTO3bsQFVVVdqH6TPPPJPVwoiIaG6VeZw41uxFJKrBwvlfKW779u14\n7bXXsH37dvz+97/Htddem7Z/y5Yt+Pu//3v4fD5IkoRDhw7he9/7Htra2vDcc8/hmWeegXnU/MsT\nqZ3Hg3IZraGhge/PBIx6f4QQCPm74etvhm/gPIb7zyMaGoAkKShfU4fiyhszzi1+cSiIV8524GBf\nrEW8yGFBZa4DIVXDe74QBkLRtOPNsoSKPDsq4q3eidZvp3nir/MBXxi/++XrMJlkrN6+Bq+/fAy1\na4tx36euM3QQv9+c+xCaS8ZNxQW4bdsKw+oA+HdrMnx/JjbTHysmDeT333//jF6AiIiuDKUeBxrP\nAV19AVSU5BhdDs0Tn/jEJ/Duu+/ivvvug9VqxQ9/+EMAwFNPPYVrr70WW7ZswTe/+U18+ctfhizL\n+NrXvgaXy4V//dd/xeDgIL7yla9ACAFJkvCzn/0srXWd6EokhI7gcDuG+8/HQnj/eajRkd5FJrMT\n+cUbUbryNjhz06cIDEQ1HOoawBvnu9E2HAQQGwxfAOgJRNATiAAA8q1mbCzKTYbvilw7ih02KPL0\nArSu6fjlMwcRCem46ePV+I+3zsKkyLj/08YN5AYAzZ2DaHVIsER17L5qavOvEy1U434qJu4R27Zt\n25wVQ0TzR319PSKRCH8RXURS5yJnIKcEWZbxyCOPjNme+oP9jh07sGPHjrT9Dz74IB588MGs10eU\nbbquIjDYNhLAB1qga+HkfrMtH+7CGrgKVsKVvwI2ZzEEAG8gjJMd/bg4HETbUBAtg34MhtPv9ZYl\nYInLhopcBypy7Via48DSXDtyLLPzw9Xrv26C5LiI1bVASziK3sEQ7rl9DZYUumbl+pdDCIGnD7cC\nsoSbnS5YOZAbLXLj/m1fv359xl/OEr9yNzU1ZbUwIiKaW8mR1nkfOREtYtFoCIP9LRjsPYfAYAsi\nw22A0JL7NXMBgvbV8JuXoN9UiiHhRCioITikIXjOi6DajUBURVQXGa+fazHhunI3rlviRlmOHaZp\ntnpP1dEP27D/rWas3irBZJbwXGIgt9uMG8gNAN6/1Id2XYW9N4S6uzcYWgvRfDBuID958uRc1kFE\nRAbjXOREdCUTAghrOkKqhmBUQ1DVYsuqhqCqIxhNXdcQUnUEVQ3RqB+OcAfy1Q54RBfcog+yJJLX\n7EU+OkQxOkQROkQRgqodCCZeNQogdg+4SZZgNymwmxTkW+3ItZrQH4riYrxremVebNT0TUW5We8u\nfunCAF7ZexRWmwlOFzDgD0HTBf7izk2wzVLr++U43efD040XIGk6tslWOJxWw2ohmi94IxcREQEA\nSgsTc5EHDK6EFrOoNvG0aplkboecfB/EhHvHnJs4XGQ4SqSvph0zsk+M2Tf6GDHqQqnH+jSgNxgB\nIJBofBVCQMTPS5wxZl/KdfX4QuyY9HNFSn2jr5tpny4ENAFouogvC2h67FlPLiNtfexxSC6nnzey\nTxcCasZrpF87pAL6bw9neHfTORBAmdSDMqkHy6VueKTB5D4dEoaUIvjMSxCyLYFmXwqrxYEik4JK\nkwKbWU6Gblv82W6SYVVkDEc1dPhC6PSFcKbPh0NdAxCY2yAOAL6hEP7rPw5A03R85s+24siJ/VA1\nga1ri3HdxtKsv/54Lg4F8cSHc3xV0QAAIABJREFU56DpAp6jfbjhM1cZVgvRfMJATkREAACX3Ywc\nhxkd7LJOBvrqFALV4iUBfzhmdBFzTpEkKBKgyBJkSYIiSZDl2LNVluPbATWkojAvF/bU0KzIcIph\nOCLtMIcuQgq0QYT7k9eWZDNc+avgKliBnIKVcOYtg6xYxq1F1QV6AmF0+EI42+9Hpy8UC+H+EMKj\nfkyqzHXgjtVl2Fw8N0EcAFRVw389/SGGB0OouXkl/vX109jgjkKWJDxw92bDBnLrDYbx+IGzCKga\nljQPIycssHJ1oSG1EM03DORERJRU6nHifPsQNF1MezRfotmwvnD8AQUljP//5OXmjKmelrh+phpG\nv7aEDMdK6fsynTv62tKohb6+PhS6Pclz5PiCFL+OBCn+jGTwyrxv5NVSj0/WLY3aFz939GvKkgQl\nHoxjITm2zSRJyX2JAK3IsWA95pzkOtKCthLfnvpnmUxDQwO2bl2JkK8rNgDbQDN83ecRDQ8lj5FN\nNrgK18FVsAKugpVw5JZDlsd+HQ6rGjr9YXT4guiIh+4OXwg9gTC0UV0cTLKEEqcVZS4bypy22LPL\njvIc25wGYCEEfvPCMbS19MFckY+n3zuPcFRDbakJVtPIOCFzbTii4rEPzmIgHMXtxQU49cYlrL1u\nGWRl7FRwRIsRAzkRZVRXVzfjeRXpylPmceJM2wB6B4MoLnAYXQ4tQg9eY+yAU/NZQ0Mfaq9abnQZ\nhhK6BjUaiD/8UCN+aNEAohEfMNSII394CZqavMEbJosLBSWbkwHc7ipNmxN8OKKiw+dLtnQnHn2h\nyJjXtpsUVOY544HbhtJ4+C50WJI/VBjpw3db8MEHrWi3Kehu64fTbsbf3FODG2vKDfs8D6sa/unA\nWXT6w9ixohi5p2L326/dVGZIPUTzEQM5EREljdxH7mcgJ6Ks0rVoMlQnQ3bEDy2avp4I31o0AE0N\nTXhNxVaAvKL1yClYCVfBClgdhRAA+kMRNPtC6PD2JEN3py8EX1Qbc418qxlrPTnJFu/SeADPs5oM\nnbt7Ii1nvXj2V8fQAgE1pOKq1UX4+mdr4MmzG1aTqgv89NB5nB8M4LpyN+5eswT/tLcJNrsZK1ax\nuzpRAgM5EREllXliIbzDG8DmVQYXQ0RXBCEEdDU0KkTHgrQ2KlSnhmyhR6d0fUk2wWR2wmIrgMns\ngGJxwmR2wGR2wmRxJJdPNfegcMPV6PCFcMIXQscZHzp8XnT6w4iMur9bAlDksKKqwJUM3IlWb4f5\nypoXu6NjCD94ah+6hA6zIuMrd6xH3faVkA287UgXAk83tuJYzxA2FuXii5sq0dE2gKHBELZsWwrF\nxO7qRAkM5ERElJSci5xTnxHNW0IIQOjQdQ1CqBC6BiE0CF2Nb9Ni23QVQmixbanH6hp0MbI/caye\nXB45Vk+5duy8kWtoaigZsiGmNjq+rFhhMjtgd5Ukg7RsdkBTnNAVBzTZAVW2QZWsiEoWRGBBRJeS\n05mFVB1hTUMorCPkH1kPqhp6A07obzelvZ5ZllAS71qeDN5OG0qcVpgXwD3Mh09243/97H0ENR2l\n+Xb8/Veuw7LSXKPLwi9PXcL+S31YkefAX9WsgEmW0HS0AwCwdjO7qxOlYiAnIqKkskLORU7Gaj3x\nQnxp9PxhImUKsNR9Im3b6GnDRh8vMGpErgzXSp+mTMSn/BKAELHzhR5/Rob9euw5sQ2JKcPSt4+9\nZvr2jPvVKA7+bi+EGNvNOtti059JEJCgQ4IumSBMDuiKB7pjGTTZCVVxQJNtiEo2qLIFUVgQhQlR\nYUJEyAjrEsKaiAXrkI5wMlCnhnmB2CTfwXEqGcuqyLCZFBSZgVUlnmRrd5nLBo99ftzfPduiqo5n\nftuEF35/FgBQU56Hv//rm2CeBy3P/7e5C79t7kap04qvXb0KVpMCIQSajnbAYjWhak2R0SUSzSsM\n5ERElFSQY4PFJLOFnAxz+ML5tHUxyTjomfaPRO6J9qXvzzwjuRSfx1tKOUZKWc+wXTKlHCMBUsoy\nJAhpZDmxT8TGMY+/lhwfBj1xbTm+KCEiVJjMNuhQICBDSDIE5Ni6JEGHDB2xbcngDBm6iC0nt4nE\nM6AD8fXY3OS6kGJzeyfXkZwTfAw145uWQgcQiT9GxAK0DKuiIM9qhs2kJEO1zSSPXVdGto/si61b\nFDkZuBsaGlC7uXKyoq54rZ1D+N/PHERz+yCsAK5bkocH//qmedENfP+lXuw9eQn5VjO+fs0q5Fhi\nUaPz0hAG+gLYWFMO0xV2SwBRtjGQE1FG9fX1iEQiqK2tNboUmkOyLKHE40Sn1w8hxLwdwIgWrnr9\nVqNLmN+m3nCcUWJObzl1Tu/4NpOcOl3Z2GnKZCl9uyUlWGcM0vF5wK0pwdpqkhdki/Vc0HWBV/7Y\njKd/fQJRVUchgA15djxw//UThvG5+jw/1jOE/zjaCrtJwd9cvQoeuzW5r+loOwBgHburE43BQE5E\nRGnKPE60dQ1jOBBFrtNidDm0yNy5Ov6FfewU3uMsZZ6DPHVO7fH2jdmeYa7xkW3pc3Un5+NOzM+N\n2KTdqfN3j57rG5IEOe26sYCb6frJucPjOyUAZ86cwfrq6uRc3bKE9FAtp68ngnbqnOB0ZfIOBPHY\nnoM4csYLl92MKsgoAPC5L18DZ4510vOz7fyAHz892AxJkvDft1Vhae7I6O6J7upmi4JVa9ldnWg0\nBnIiIkpTWhgbab2z189ATnOubjVb0MYTaAVWuV1Gl0Fz7K2DF/HPvzwKfzCKrdVFyO0JwB8M4lOf\n24qypflGl4dOXwj/34fnENF0PLB1JdaM+n+0p3MYvT1+rNtcBrOF0YNoNONvNiEionmljCOtExEZ\nzheI4B//80P86JkGqJqOr35mM1ZDgr8viBs+ugobt5YbXSIGQhE8duAsfBEVn9+4DDWlY38gSIyu\nzu7qRJnxZyoiIkqTmPqMI60TERnjyOkePLbnILyDIVQvK8A37tuKE+9fQPMpL1atLcatn1hrdIkI\nRFU8duAseoMR3LmmDDctK8x4XNPRDigmGavXlcxxhURXBgZyIiJKk5j6rNMbMLgSIqLFJRzV8PNX\nT+Dlt5shyxI+9/G12HXrapw40o73/nAOniIn7v78VsiyseMBRDUdTzQ049JwCB+tLMInq0ozHuft\n9qG7cxhrNpTAamPsIMqEfzOIKKO6ujo0NDQYXQYZoLjAAVliCzkR0VxqvjSIHz3TgLauYZQXOfGN\n+2qxZlkB2tsG8MpzR2C1mXDPl66GzW6e1nVn+/NcFwL/evg8Tvf5UFuaj3vXLx13Rg52VyeaHAM5\nERGlMZtkFObbeQ85EdEc0HSBF988i2dea4KqCXxy+wr8ed162Cwm+IbD+K9/PwBV07Hzi9tQWJJj\naK1CCDxzrA2HugZR7XHhv21ZPuHo/ScbOyArEtasZ3d1ovEwkBMR0RilHieOnvUiHNVgNStGl0NE\ntCB19vrx418cxInzfSjIseJv7q1B7dpYeNVUHXuf/hBDgyHc+om18yLUvnKmA2+3eVGRa8dXt1bB\nrIw/PnR/bwAdFwdRtbYIdgdn7CAaDwM5ERGNUVYYC+RdvX4sK801uhwiogVFCIE3DrThqV81IhhW\ncf2mMvw/O7cgzzUyp/hrvzqGtvN92HDVEmy/dZWB1ca82dqDV852otBuwd9cvQqOSX6sTXRXX795\nyVyUR3TFYiAnIqIxSpNTnwUYyImIZtGgL4wnnj+CfY0dsFtNePCzNfhobUXafdgfvteChn2tKF2S\nizt2bxn3Hu250tDRj2ePtyHHYsKD16xCnnXy+9ibGjsgyRKqNxjfsk80nzGQExHRGGWc+oyIaNZ9\n2NSFx587hIHhMDas9ODBz25FiduRdkxrcy9ee/EYHE4Ldn/palisxn5dP9U7jP9zpAUWRcbfXL0K\nxU7bpOcMDQRxqbUfK1YXwpHS6k9EYzGQE1FG9fX1iEQiqK2tNboUMkCpJ/YFsdPLQE5ENFOhsIqf\nvXIcv9nXApMi4c8/uR533bIKyqjpywb7A9j79IcAgJ1frEX+qLB+OWbyed42FMATDecgBPDVbStR\nmTe1epoaObo60VQxkBMR0RiJucjZQk5ENDOnL/Tj0Wca0O71o7I0B9/8XC1WLMkbc1w0ouK5fz+A\ngC+CP717E5ZXFRpQ7YieQBiPHziLoKrjK1ctx/rCqd++1HS0A5CAtRszz09ORCMYyImIaAyHzYxc\np4VTnxERXSZV07H39dPY8/pp6LrAXTdX4Qt/ug6WDIOhCSHw8nNH0HlpCFuvW4ZtN1QaUPGI4XAU\nj31wFoNhFfeuX4prlrinfK5vKIQL5/uwbIUbrtzJu7cTLXYM5ERElFGZx4lzlwag6WJMt0oiIhrf\npR4f/vezDTh9YQCF+XZ8/d4abFldNO7x7/3hHI4fbkfF8gL86ac3GTqIW0jV8PiH59AdCONPq0pw\n2/LiaZ1/8lgnIIB1m9hdnWgqGMiJiCijUo8Tpy70o3cgiOJZuI+RiGih8gUiONnaj6aWPpxs6UNT\nSx+iqo5bti7FX969GS77+KOSn2nqwhuvNiEnz4ZdX9wGxTT+3N7Zpuo6/vlgM1oHA9i+1INPr5n+\nlGWJ6c7WMpATTQkDORERZVRaGAvhHb1+BnIiojghBDq8fpw434eTrX04cb4PbV3Dyf2SBFSW5mL3\nbWtwY035hNfydvvwy/88CJMi454vXW1oF29dCPz70Vac8A5jc3EuvrBx2bRb6gO+MFrO9aJ8WT7y\nCuxZqpRoYWEgJ6KM6urq0NDQYHQZZKCy5Fzk/gm7WhIRLWSRqIYzbQPJlu+mlj4M+SPJ/TaLgi2r\nC7F2uRvrlrtRXemesEU8IRSM4rmffYBwSMVd99VgSUV+Vuqfyue5EAJ7my7hg/Z+VOU7cX/Nysu6\nVenU8S4IXWDd5um3rBMtVgzkRESUUWliLnJOfUZEi0j/UCgZvJta+nDu4gBUTST3FxXYcdOacqyL\nB/DlZblQlOl1M9d1gRefOYjeHj+uv6UKm2uXzvYfY1p+29yF11u6Ueay4WvbqmCd5p8n4cTRdgCc\n7oxoOhjIiYgoo8TUZ529AYMrISLKDk0XuNA5hJMtfTgRv/879d88RZawsjwvFr5XuLG20o3C/Jl3\nxX7ztZM409SNlWuKcNsn1834ejPx3sVevHCqHQU2M75+9So4LZcXD0LBKM6f8aJsaR4KPLzNiWiq\nGMiJiCijghwrrBaFc5ET0YIRCEVx+kI/ms7HWr9PXehHIKQm97vsZmxbV5IM4Ksr8mG7zIA6nuOH\nLuGPb5yFu9CJz3xhK2QDZ7E42j2Ipxtb4TAr+PrVq+C2Wy77WqePd0LXBAdzI5omBnIiIspIkiSU\nuh3o7PVDCGHoNDxERNMlhEB3fzDW9fx8L0629KOlYxD6SO9zlBe5cMOmWPhet9yN8iJXVgNy56VB\nvPTcYVisCu750tWwOy4/AM/UuX4//uVgM0yShL/eVoUlOTNr+T8RH119/RYGcqLpYCAnIqJxlXqc\naO0cxpA/gjyX1ehyiIjGpWoCpy/0x0Y/j9//3TcUSu63mGSsW+FJ3vtdXVkwp/+u+X1hPPfvB6BG\nddzzpatRVJozZ689WocviH/68CxUIfDVrVWoKnDN6HrhkIpzp3pQXJoDT9HMrkW02DCQE1FG9fX1\niEQiqK2tNboUMtDIfeR+BnIimlNCCIQiGob9EQwHEo/oyLJ/ZHnIF8G5S/1QtUvJ8wtyrLhhcxnW\nLfdg3fICrCzPh9mgOb41TcfzP2/AYH8Qt3y8GtUbS+fstUd/nvcFI3jsg7PwRzX8+aZKbCnJm/Fr\nnGnqgqbqWMvB3IimjYGciIjGlRxpvTeA6kq3wdUQ0ZUqHNXgC0Qw5I/AF4hiKBBJW08N3LFtsWVV\n06d0fUWWUJhrRu36+OjnKzwoLrDPm1tt/u9Lx9F6rhfrNpfhxttXG1aHP6ri8QNn0ReK4u7qJdhe\n4ZmV6zYluqszkBNNGwM5ERGNK3UuciJaPIQQUDUBVdOhaTqimg5Vja0nHoGQmhKex2+9Hg5EEYlq\nU3pdSYoNrJbjsKC4wIEcpwUuhxm5DgtcDgtyHWa4HBbkOC3IccSOy3FY4LCZcPDgQdTWbsnyOzN9\nB/e34sC7LSgpy8Wd915l2I8EEU3HTz48h3ZfCLctL8LHV5bMynWjERVnT3bDU+Q0tBs+0ZWKgZyI\niMZVWhibuoZzkdNcCceDmxDxkbdSnpLb4oRI7o6tjBwe25fx+LHXTXu9kUslr6ELkVwWiM0hLeLb\n0vYl1yfep6dsy7QPGY8VONfsR3ekBao6EopVNR6WNZESnBP7YwE6qsZCdXI97Zj4si7St2np793l\ncNpMcDksWFaagxy7OR6iE4/U9XiwdlrgtJkNHXV8tl0434dXf9kIu8OM3V+6GharcV+9nzp0Hmf7\n/bimrAC71y2dtR8Gzp7sQTSiYd3msnnTI4HoSsJATkRE4youcECWJbaQ05zZ+e16o0uY397vn9Hp\niizBZJJhSjwrsYfVIsNskmFSJCjxbWZFjh8jJY8zJbbJEuw2E3KdFrjslthzSou1y2GGSTHmfu35\nYmggiL1PfwghgJ1/ts3QublDOnCkexDrPDn40pZKyLMYnBPd1dexuzrRZWEgJyKicZkUGUX5dgZy\nmjNbq4uBeFZIRIbUVjcpuU9KW0/bN8HxkMZeN3m0NPa6sixBkpAMMLH1kW2ShFHrEuSUbZPtG1mP\nLSPjsbHjL1xoxeqqlTCZ4mFZkWEySVDkRJiOh+eUoJ3YZjbJUGR5QbU+z2fRqIb/+o8D8A+H8fG7\nNmLF6sI5r8EfVbH/Uh8GwlFoAqjMdeCrtSthkmfvhxI1quH0iS7kux0oLZ/54HBEixEDORFlVFdX\nh4aGBqPLoHmg1OPAkTNehCIqbBZ+bFB2/Y/7rze6hHmrweRF7dalRpdBkxC6QP3eI2hvG8RV11Tg\n6o8sn7vXFgLn+v14u82LDzv6EdUFFMdyLLfo+Ourq2AzKbP6eudO9yASVlF7fSW7qxNdJn6zIiKi\nCZV6nDhyxouu3gAqy3KNLoeIaF6KhFUcOdCG9985jz6vH+WVBfjEZzbNSVD1RWKt4W+3edHhi829\nXuyw4qZlhbi+3I0zx44i12qe9dc9ye7qRDPGQE5ERBMqS0595mcgJyIaZWggiA/+2IKD+1sRCkah\nmGTUXLMMt35yLUyz3CKdSgiB030+vNPmRUPnAFRdwCRLuLqsADctK0S125XVHwM0Vcep413IzbOh\nvCI/a69DtNAxkBMR0YRKCzn1GRHRaO1tA9j/VjNOHGmHrgs4XRbc/LFqbLu+Es4ca9ZedziiYt/F\nXrzT5kWnPwwAKHVacWNFIa5f6kHOHN1adP6sF6FgFJu3LYXEsQmILhsDORERTSjZQs6pz4hokdN1\ngdPHO7H/7WZcaO4DABSX5uC6m1diY005TObstIjrQuBUb6w1/FDXSGv4dUvcuHGZB6sLstsansnJ\nRnZXJ5oNDORERDSh0vhUPZ29AYMrISIyRiSs4vAHbXj/nWb0x/8tXLW2GNfdvBIrVhdmLQwPhaN4\n72Iv3mnrRXcg1hpe5rLhporYveFOgwba1DUdJxs74cyxomK525AaiBYKBnIiyqi+vh6RSAS1tbVG\nl0IGc9jMyHNZ0MEu60S0yAz2B/HBH8/j4P5WhEMqTCYZW69bhmtvWomikpysvKYuBE56h/F2mxeH\nuwagCcAsS7i+3I2blhWiKt85rR8AsvF53nq+DwF/BNtuqORUekQzxEBORESTKvU4cbZtAJqmQ1Fm\nbw5bIqL56NKF/tj94Uc7IHQBZ44V199ShdrrK+F0Zef+8MFwFO+2xe4N9wYjAIDynFhr+LXlbjjN\n8+dre9ORWHf1tZvYXZ1oprL+N/uRRx7BkSNHIEkSvvvd72LTpk3Jfe+99x5+/OMfQ1EU3HzzzXjg\ngQfw/PPP46WXXoIkSRBC4Pjx4zh48GC2yyQiogmUeZw41dqPnoEgSuP3lBMRLSS6LnDqWCf2v3UO\nbS39AICSslxce9NKbNy6JCsjputC4IR3CG9f8OJo9yA0AVgUGduXenDTskKsyHPMu/m9hS5wsrED\ndocZy6s8RpdDdMXLaiA/cOAAWltbsWfPHpw7dw7f+973sGfPnuT+hx9+GD/72c9QXFyMz3/+89ix\nYwd27tyJnTt3Js9/7bXXslkiERFNQSKEd/b6GciJaEEJh1Qc/uAC3n/nPAb6YveHr15XjGtvyt79\n4f2hCN692Is/tvWiN94aXpFrj7WGL3HDnqXB4WZDW0sffMNh1FyzDDJ7TBHNWFYD+b59+3D77bcD\nAKqqqjA0NAS/3w+n04m2tjbk5+ejpKQEAHDzzTdj//79qKqqSp7/xBNP4NFHH81miURENAVlhbGB\n3Tp6A7jK4FqIiGbDQF8AH/zxPA69fyF5f3jt9ZW49sYVKMzC/eG6EDjWE2sNb+wZhC4AqyLjpopC\n3FjhQeU8bA3PpCkxuvoWdlcnmg1ZDeRerxcbN25MrhcUFMDr9cLpdMLr9cLtHhmV0e12o62tLbne\n2NiIsrIyeDzsCkNEZLRkCzmnPiOiK9zF1tj94U2NsfvDXTlW3PDRKtReVwlHFu4P7wtG8MeLvXi3\nzYu+UBQAUJnnwE0VhbhmSQFsWegKny1CCDQd7YDVZsKKVYVGl0O0IMzp6BBCiCnv27t3L+6+++4p\nX7uhoeGy61oM+P6Mj+9NZmVlsV+++f5MbLG8P8NBDQDQdO4SGhpCUzpnsbw3RDT/6ZqOk8c6sf+t\nZlxsjd8fviQX1928Ehuumv37wzVdoLFnEG9f8OJYzxAEAJtJxi3LCnFjRSGW5Tlm9fUmUldXN2v/\nHre3DWBoIITNtUuhmNhdnWg2ZDWQFxcXw+v1Jte7u7tRVFSU3NfT05Pc19XVheLi4uT6Bx98gIce\nemjKr8WpmcbX0NDA92ccfG8mxvdnYovp/RFC4Ilf/xohzTylP/Niem8uB3+sIJob4VAUh96P3R8+\n2B8EAKxeX4Lrbl6J5VWeGXURV3WB/lAEvcH4IxCGNxhBXzCCdl8IwxEVALAiP9YafnVZAaxXUGt4\nJk1H493VN7O7OtFsyWog3759O37yk59g9+7dOH78OEpKSuBwxH4RLC8vh9/vR3t7O4qLi/Hmm28m\n7xfv7u6G0+mEyTR/pncgIlrMJElCqceJzl4/hBBXxH2ORLR4DfQF8P47sfvDI2EVJrOMbTdU4pob\nV6Kw2DWla0Q1Hf2hCLzB1NCdWA6jPxRFpr6fEoB8mxkfrSzCTRUeLM2du9bwbEp0V7dYFVRVFxld\nDtGCkdXEW1NTgw0bNuDee++Foih46KGH8OKLLyInJwe33347vv/97+Mb3/gGgFh3msrKSgBAT08P\n7x0nIppnSj0OtHQMYdAXQX5OdubhJSKaibaWPux/qxknGzsgBODKteIjt63C1usq4XBa0o6NaDr6\ngpFkq7Y3GE4L3oPh8QN3gc2MVQUueOwWeByW2LPdgkK7BQU2C8wLcPTxrvYh9PcGYl385/Eo8ERX\nmqw3QScCd0J1dXVyedu2bWnToCVs2LABTz31VLZLIyKiaUid+oyBnIjmg0hYRZ/Xj672Ibz7ux4M\n9LYDAErLc1F700oUr/JgIBLFB95BeNtSgncggqF4l/LRZAkosFmwxu2COx6yPQ5rMnQX2CwwyYuv\nl9AJdlcnygr2CScioikpK4wF8o5eP9Yud09yNBHRzOhCIKrp8AWj6On1wdsbQG9fAH2DQQwMhTA4\nHEYwrEIoEoQiQSuww762EEqeDSeFjgPdPUB3z5jrKpIEt92CtTn2ZKu2225BocMCj92KfKsZyiIM\n3BMRQqDpSDtMZhmr1hZPfgIRTRkDORFlVF9fj0gkwoG5KIlTnxEtbkIICMSCsi4AVdcR0UYeYU2M\nrI/aN/oRTl3XdYSjGkJRDSFVQ1QTiAoBfbxM7ADgsAKlY3vqDAMwRaPw2C2oyLXHW7WtyeDtcViQ\nZzVDXkTjYMzG53lPlw+9PX6s3VQKi5XxgWg28W8UERFNSZlnpIWcKFvebI21aKbduytSF0WmzWnG\nm2U17dxxjxl9rZGzhEjsj2+Lr4v4TpFyDSHGOQ4i+dojx43UNvIaI1PCJo7rHgIaj7bGA3EsFOsi\ndj0tvi01MI8+LrEshICO2NRcAqn7E9cT0FKW9ZRrZoUQkLT4Q489m+LLZlmCzazAbjHBYTPDZTcj\n12lBjssKu9kEiyLHHxI6zzfjhprNyLWaFlXgnguJ0dXXb15icCVECw8DORERTUlRgR2yLKGzN2B0\nKbSAPXO8zegS5jEJuNg73TMgSxJkaeRZkiTIkgQlsQwJiiTBLKcel3psfBskyDJiz5IEsyLBIssp\noViGCYAa1hD1RxD2RxEaDiEwFIa/P4jgUDgZuOVE+NYF8vLscHuc8BQ54S4ceRR4HNMaPKzhUmx0\nc5p9TUfboSgyVq9nd3Wi2cZATkREU2JSZBQX2NlCTll1/1XLR1ZSWjlT2zvHa/tMbxQd59xxTpbG\nWZOkkbXYuRKkxBHJfdLlHQckpxBMPS712JHjgBPHT2DTxo1p4To1PMuSFA/Y6eF7Nui6QDSiIRJR\nEQmriIQ1DA0E0ef1o887jN4eP7p7/RgcCGbsupCbZ8Oy4tx42HbBXeiAu8iFAo8DZo7YPa/19vjQ\n3TGMNetLYOUPHkSzjoGciIimrNTjxOHTPQiGVdh5HyFlwdVLOGDgeNpNQLFz4hkOhC4QiWgIR1RE\nIlosPMefo8ltifWRgB1NPTaiIRpWY/vi29WoPml9OXk2VK70JFu4Ey3eBYVOhu4rWBNHVyfKKn6b\nIiKiKSvzOHEYPejs9WPFkjyjyyGaNhG/5zp5z7eI378tUvYhfi+3ENB1AaHHnnVdh64j/py6PfWh\nQ9dE8tzRy7qIP+t6bLs8QMI2AAAgAElEQVQW35eyLFKOSVxX6AIdHQO4cOpghkCtJcN2NKLN+D1S\nFBlmiwKLVYHDaUF+gR1miwkWqwkWiwKLxQSzVUFOri2tezkH+1qYmo52QJYlrNlQYnQpRAsS/+Uk\noozq6urQ0NBgdBk0z6TORc5ATtnw4//xu5GB1FJDc1pYHhkALTFImoiPhpYaskcH74VhZAwHWZaS\nIdnusCA3X0muxwJ0bN0cD9EWa/zZosCcCNcpz4njFJNs4J+PZttMPs8H+gLouDiIquoi2B2WWa6M\niAAGciIimoayQgcAoMPLgd0oO8yWWNfm5P3T8ZuuJUlKuc9aGrnfOj5ftJRyE7aUel58Z+r1kvd4\nJ7elHp9+DVmRIMsSZFmGJAOyLMfXRx7SmOWxx6QfJ8cGR5NlyEr8vm9ZGrMsSxJkJXYtSZJw6nQT\namo2x0M0gzNlH7urE2UfAzkREU1Zags5UTb89+/canQJ81Z7lxl5BQ6jy6BFpOloByQJqN5YanQp\nRAsWf1olIqIpK+Vc5EREi8LQQBAXW/tRWeWB0zXxYIJEdPkYyImIaMrsVhPyc6xsISciWuBONnYC\nANZtXmJwJUQLGwM5ERFNS5nHie7+IFRt8mmQiIjoynTiaDsgAWs3sbs6UTYxkBNRRvX19WhtbTW6\nDJqHSj0O6LpAT3/Q6FKIiGgSl/N57hsO48L5PlQsdyMn15alyogIYCAnIqJpKuN95EREC9rJxg5A\ncHR1ornAQE5ERNNSWsiR1omIFrLkdGfsrk6UdQzkREQ0LckWci8DORHRQhPwR9ByrhdLluVzmj2i\nOcBATkRE08K5yImIFq5TxzohdIF1m9hdnWguMJATEdG05LkssFsVdPYGjC6FiIhmWVNjvLs67x8n\nmhMM5ESUUV1dHSorK40ug+YhSZJQ6nGis9cPIYTR5RAR0QSm83keCkbRfLoHpUty4Y6PF0JE2cVA\nTkRE01bqcSIU0TDgCxtdChERzZLTJ7qgawLrtrB1nGiuMJATEdG0JQZ26/Sy2zoR0ULRdKQdAHj/\nONEcYiAnIqJpS0x9xrnIiYgWhnBIxdlTPSgqzUFhSY7R5RAtGgzkREQ0bWWe2FQ4HGmdiGhhONvU\nBU3V2TpONMcYyImIaNoSU5+xhZyIaGHg6OpExmAgJ6KM6uvr0draanQZNE8V5duhyBI6vQzkRETz\n2VQ+z6MRFWeauuEudKK4jN3VieYSAzkREU2bosgodjs4FzkR0QJw7lQPohEN6zaXQZIko8shWlQY\nyImI6LKUeZwY8IURCEWNLoWIiGbgxBF2VycyCgM5ERFdltL4wG5dfWwlJyK6UqmqhjNNXch321G2\nNM/ocogWHQZyIiK6LGWJqc94HzkR0RWr+bQX4ZCKtZvYXZ3ICAzkRER0WRIjrXPqMyKiK1fTUXZX\nJzISAzkRZVRXV4fKykqjy6B5rCw59Rm7rBMRzVcTfZ5rmo5TxzqRk2fD0mUFc1wZEQEM5EREdJlK\n4veQc+ozIqIrU8tZL0LBKNZtKoMks7s6kREYyImI6LLYLCa4c63oYJd1IqIrErurExmPgZyIiC5b\nqceJnoEgVE03uhTKElVV8bd/+7e477778IUvfAEXL14cc8zLL7+MnTt34p577sHzzz+fts/r9eKa\na67BgQMH5qpkIpoCXRc4eawTTpcFFSvcRpdDtGgxkBMR0WUr9Tih6wLd/byPfKGqr69HXl4enn32\nWfzVX/0VHn300bT9wWAQTz75JJ5++mn8/Oc/x9NPP42hoaHk/n/8x39ERUXFXJdNRJO40NyLgC+C\ntZvKILO7OpFhGMiJiOiyJaY+6/QykC9U+/btw+233w4AuOGGG3Dw4MG0/UeOHMHmzZvhdDphtVqx\ndevW5DH79++Hy+XCmjVr5rxuIpoYu6sTzQ8M5ESUUX19PVpbW40ug+a50uRI67yPfKHyer1wu2Pd\nWSVJgizLUFU1434AcLvd6OnpQTQaxRNPPIEHH3xwzmsmohGZPs+FLtDU2AG7w4zKKo9BlRERAJiM\nLoCIiK5cZYmR1hnIF4S9e/fi+eefhyTFuq8KIXD06NG0Y3R94vEChBAAgKeeegq7d++Gy+VK2z6Z\nhoaG6Za9qPD9mRjfn7EikQiA9PemrycM31AYS1c6cPjwIaNKm1f4/87E+P5kDwM5ERFdtmQLOac+\nWxB27dqFXbt2pW37zne+A6/Xi+rq6mTLuMk08vWhuLgYPT09yfWuri7U1NTgxRdfxDvvvIP//M//\nxIULF9DY2IjHH38cVVVVE9ZQW1s7i3+ihaWhoYHvzwT4/mTW0dGBSCSS9t789qXjAHpx460bsXpd\niXHFzRP8f2difH8mNtMfK9hlnYiILluu0wKHzcQW8gVs+/bteO211wAAv//973Httdem7d+yZQuO\nHTsGn88Hv9+PQ4cOoba2Fs8++yz27NmD5557Drfccgu+//3vTxrGiSj7hBA42dgBq82EFasLjS6H\naNFjCzkREV02SZJQ6nHiUo8PQohkV2daOD7xiU/g3XffxX333Qer1Yof/vCHAGJd0q+99lps2bIF\n3/zmN/HlL38Zsizja1/7WrKbOhHNP+1tgxjsD2JTbTlMJsXocogWPQZyIiKakTKPE82XBtE/HIY7\n12Z0OTTLZFnGI488Mmb7/fffn1zesWMHduzYMe41Mp1PRMZIjK6+fvMSgyshIoBd1oloHHV1dais\nrDS6DLoClMYHduN95ERE80/q57kQAk1H22G2KFhZXWRwZUQEMJATEdEMJeci533kRETzWlfHEPp7\nA1i9rgRmM7urE80HDORERDQjnIuciOjK0HQk3l19S5nBlRBRAgM5ERHNSFk8kHd6AwZXQkREE2lq\n7IDJJGPV2mKjSyGiOAZyIiKaEU++HSZFYpd1IqJ5rKdzGN4uH1atK4bFynGdieYLBnIiIpoRRZZQ\n4nawyzoR0TzW1Bjrrr5uE7urE80nDORElFF9fT1aW1uNLoOuEKUeJ4b8EQRCUaNLISKiFInP86Yj\nHVAUGavXlxhdEhGlYCAnIqIZS9xHzqnPiIjmH10X6OoYwso1hbDZzUaXQ0QpGMiJiGjGSpNTn3Fg\nNyKi+UbTBABg3eYlBldCRKMxkBMR0YyVuh0AOPUZEdF8pKkCsiyheiO7qxPNNwzkREQ0YyMt5Azk\nRETzia4LCB1YvsoDu8NidDlENAoDORERzVgp7yEnIpqXwiEVALurE81XDORElFFdXR0qKyuNLoOu\nEFazAneujS3kRETzSPPpHjS9b0Jnswuba8uNLoeIMmAgJyKiWVFW6IR3IIioqhtdChHRohcMRPDS\nnsOQZQlXXZ8Ps8VkdElElAEDORERzYpSjwO6ALr7OdI6EZHRXn2hEcODIdy0Yw3yPbx3nGi+YiAn\nIqJZwbnIiYjmh8aDF3H8cDuWVhbgI7euMrocIpoAAzkREc2KxMBuvI+ciMg4g/0BvPpCIyxWBXfd\nVwNZ4dd9ovmMf0OJiGhWlMWnPuNc5ERExhC6wEt7DiMcUvGxOzfCHf93mYjmLwZyIsqovr4era2t\nRpdBV5BkC7mX95ATERlh/9vNaDnbi+qNpbjqmgoA/Dwnmu+yHsgfeeQR3HvvvfjsZz+LxsbGtH3v\nvfcedu3ahXvvvRdPPvlkcvvLL7+MO++8E5/5zGfw1ltvZbtEIiKaBTkOM5w2E1vIiYgM0NU+hN+/\nehLOHCvqdm2GJElGl0REU5DVQH7gwAG0trZiz549+MEPfoCHH344bf/DDz+Mn/zkJ/jFL36Bd999\nF+fOncPAwACeeOIJ7NmzB//yL/+CN954I5slEhHRLJEkCaWFTnT1+qELYXQ5RESLhhrV8OKzB6Fp\nOu7YvQVOl9XokohoirI6IeG+fftw++23AwCqqqowNDQEv98Pp9OJtrY25Ofno6SkBABw8803Y//+\n/SgoKMD27dtht9tht9vxD//wD9kskYiIZlGpx4lzFwfhC3IuciKiufL735xEd8cwaq+vxJr1JUaX\nQ0TTkNUWcq/XC7fbnVwvKCiA1+vNuM/tdqO7uxuXLl1CMBjEAw88gM9//vPYt29fNkskIqJZlJj6\nrM+nGlwJEdHicP6MF/vfaoa70Ik/uWO90eUQ0TRltYV8NDFBF8bEPiEEBgYG8OSTT+LixYv4sz/7\nM/zhD3+YqxKJiGgGEgO79Q8zkBMRZVswEMFLvzgESZbw6c/VwGKd06/2RDQLsvq3tri4ONkiDgDd\n3d0oKipK7uvp6Unu6+rqQnFxMRwOB2pqaiBJEioqKuB0OtHX15fWmp5JQ0NDdv4QCwTfn/Hxvcms\nrKwMAN+fyfD9STfcFwIQayHne0NElF2/+eUxDA2GcPPHqlG+rCDjMXV1dfz3mGgey2og3759O37y\nk59g9+7dOH78OEpKSuBwOAAA5eXl8Pv9aG9vR3FxMd588008+uijsNls+O53v4uvfOUrGBgYQCAQ\nmDSMA0BtbW02/yhXtIaGBr4/4+B7MzG+PxPj+zNWRX8AT7/xO/T7VL43E+CXYyKaqWMHL+HYoUso\nryzAjbetMrocIrpMWQ3kNTU12LBhA+69914oioKHHnoIL774InJycnD77bfj+9//Pr7xjW8AiP16\nV1lZCQD42Mc+ht27d0OSJDz00EPZLJGIiGaRJ88OkyKjj13WiYiyZrA/iF+/cBRmi4JP31cDWcn6\nTMZElCVZv9EkEbgTqqurk8vbtm3Dnj17xpyze/du7N69O9ulERHRLFNkCSVuB/oGA0aXQkS0IAld\n4KU9hxAOqajbtRnuQqfRJRHRDPDnNCIimlVlhU4EIzp8wajRpRARLTj732lGy9lerNlQgpprlxld\nDhHNEAM5ERHNqlJPbKyQTq/f4EqIiBaWro4h/P7XJ+F0WXDHri2QJMnokohohhjIiSij+vp6tLa2\nGl0GXYGWl+UBAN48eNHgSoiIFg5V1fDiMwehaTruuOcqOHOsUzqPn+dE8xsDORERzaqP1i5FgUvB\nK39sRmvnkNHlEBEtCH/4zSl0dwyj9vpKrFlfYnQ5RDRLGMiJiGhWWcwKPl6bD10XeOrFRgghjC6J\niOiK1nLWi31vnYO70Ik/uWO90eUQ0SxiICciollXXW7HtnUlOHrWi3ePthtdDhHRFSsUjOJXvzgE\nSZLw6c/VwGLN+iRJRDSHGMiJiCgrvnLXRpgUGf/20jGEwpyXnIjocrz6QiOGBkK46fbVKF9WYHQ5\nRDTLGMiJiCgrlhS6cPdHV8E7GMJ/vXHa6HKIiK44xw5dwrFDl1C+LB833r7a6HKIKAsYyIkoo7q6\nOlRWVhpdBl3hdt26GoX5drz45jm09/iMLoeI6Iox2B/Eqy80wmxRcNd9NZCVy/vazs9zovmNgZyI\niLLGZjXhLz61Eaqm46lfcYA3IqKpELrAS3sOIxSMYsenNsBT5DK6JCLKEgZyIiLKqhs2l2HL6kI0\nnOzGgRNdRpdDRDTvvf9OM1rOerF6fQm2XrfM6HKIKIsYyImIKKskScL9d22CIkt46leNiEQ1o0si\nIpq3ujuG8MarJ+FwWXDH7i2QJMnokogoixjIiYgo65aV5uKOG1eiqy+AX7551uhyiIjmJVXV8OIz\nh6CpOu7YvQWuHKvRJRFRljGQExHRnPjsjmoU5Fix9/XT6OoLGF0OEdG884ffnEJXxxC2XrcM1RtK\njS6HiOYAAzkRZVRfX4/W1lajy6AFxGEz40t3bEBE1fFvLx8zuhwionml5ZwX+946B3ehEzs+tWHW\nrsvPc6L5jYGciIjmzC1bl2L9Cjf2NXbg4Kluo8shIpoXQsEoXvrFYUiShLvuq4HFajK6JCKaIwzk\nREQ0ZyRJwl/dvRmyBDz1YiOiqm50SUREhvvNi40Y7A/ixttWY2llgdHlENEcYiAnIqI5tWJJHj5x\nwwpc6vHhlXfOGV0OEZGhjh9uR2PDJSypyMeNf7La6HKIaI4xkBMR0Zz73MfXItdpwZ7fnULvYNDo\ncoiIDDE0EMSvnz8Ks0XBpz9XA0XhV3OixYZ/64mIaM65HBZ88ZPrEQxr+PdXThhdDhHRnBO6wEt7\nDiMUjGLHp9bDU+QyuiQiMgADORFlVFdXh8rKSqPLoAXs9quX/f/t3Xl0UwXe//F3km4USvcUSlkr\nq1CQIusg6AAFhIICyiNQ6+548Djzw3EZEJ3fwJGjAiLIUQf9KYM8fR4QRqgKAlJBqBTKpqxapCxl\n2qbsoTRtk98fhbK1ASrNTcvndeCkuffm5tPvafPtN7m5oWXjEL7fdoSfs2xGxxER8ahNP/zGb7/Y\naNkuis7dq6/fqp+LeDcN5CIiYgizuewEbyYTfLj0J0pLdYI3Ebk95B07zZqv9hBYz4+hD3XEZDIZ\nHUlEDKKBXEREDNOqSSj9uzbl4LHTfL3xoNFxRESqXUlJKUsXbqO0xMnQUR2pF+RvdCQRMZAGchER\nMVTS4LbUrePL5yv2cPJMkdFxRESqVdqKfeTmnOaubk1o3b6B0XFExGAayEVExFDB9fwZN7AN9vMl\nzP9aJ3gTkdrrYJaNjWlZhIYHkjDsTqPjiIgX0EAuIiKGG9ijGc2j67Mq4xD7so8bHUdE5JY7X1jM\nl/+9HRMw/JG78PP3MTqSiHgBDeQiUqHU1FSys7ONjiG3CYvFzDMPxAHwwZKdlDpdBicSEbm1Viz9\nmVMnCvlDv5Y0bhbmsftVPxfxbhrIRUTEK9zZIpy+8TH8euQUqzbpj0cRqT1278hhZ+YRohuHcE//\nVkbHEREvooFcRES8xmND7qSOv4X5X+/mtN1hdBwRkd/t9KlCUhftxMfXzPBH7sJi0Z/fInKJHhFE\nRMRrhNUP4L8GtOHMuWIWrNhjdBwRkd/F5XSxLGU75wuLGZB4JxHWekZHEhEvo4FcRES8ytDeLWgc\nVY8V6Qf59chJo+OIiFRZxobfOLDfxh1trcT3aGp0HBHxQhrIRUTEq/hYzDw9vAMuF3y4ZCdOneBN\nRGqgvP+cYU3qHgLr+pH4UEdMJpPRkUTEC2kgF5EKDRkyhKZN9Wy+GKNTKyu94qLZm32CtK2HjY4j\nInJTSkuc/PvzrZSUOBkyKo569QMMy6J+LuLdNJCLiIhXejzxTvx8Lfy/1N3YC4uNjiMicsPWrtjH\nf3JO06lrY9p0aGh0HBHxYhrIRUTEK1lDA3moX0tOniniv7/dZ3QcEZHrsuWeIXXRDjam/UpoeCAJ\nw9obHUlEvJyP0QFEREQq80CfO1iTcZjlPxygf7cmNG1Q3+hIIiJXcLlcZB8oID3tAL/szgUgNDyQ\nEePi8Q/Qn9oi4p4eJURExGv5+Vp4anh7/u/Hm/ho6U9MebanTowkIl7BWepkz85jpH+fRc7hUwDE\nNA2lR99YWrdvgNmsxyoRuT4N5CIi4tXubteAu9tFsXl3Lj/syKF3p0ZGRxKR21jR+RK2ZRxi07oD\nnDpRCCZo06EBPfrE0rh5mNHxRKSG0UAuIhVKTU3F4XAQHx9vdBQRnhrWgW378vlk2c90aRtFHX+1\nLxHxrNOnCslYf5DM9IMUnS/Bx9dMl57N6N6nBWERdY2OVyn1cxHvpr9oRETE6zWMqMuIe+/gf1bv\nZ9Ga/SQNbmd0JBG5TeQeO82PaVn8tO0ozlIXdev50WNga7r0aEpgPX+j44lIDaeBXEREaoSRf2zJ\nd5mHWZr2K3+8uwmNIusZHUlEaimXy8WB/TbS07I4sD8fgAhrPbr3aUFcfAw+vhaDE4pIbaGBXERE\naoQAPx+eSGzPtM8289G/f+KNJ7vrBG8ickuVljjZtf0o6WkHyD12GoCmseH06BtLyzZWTDpRm4jc\nYhrIRUSkxujZoSGdWkaydW8eGbv+Q7f2DY2OJCK1wPnCYjLTs8lY/xtnTp/HZDZxZ6doevSNJbpx\niNHxRKQW00AuIiI1hslk4ukHOvD8O2v555c/06m1FX8dOioiVXTy+Dk2rf+NbZuycRSV4utnods9\nzenWuwUhYYFGxxOR24AGchGp0JAhQ8jMzDQ6hsg1GkcFkXhPLEvTfmXJ2l/5rwGtjY4kIjVMzuGT\n/Pj9AXbtyMHldBFUP4De/VrRuXsT6gT6GR3vllI/F/FuGshFRKTGGd2/Fd9vPcziNfu5r0tjovRK\nlohch8vp4pe9eaSnZZGdVQCAtWEQPfrG0r5TIyw+ZoMTisjtSAO5iIjUOIEBvjw25E6mL9zKx8t+\n5m/JXY2OJCJeqqS4lJ+2HiX9+yxsuWcBaNEqgh59Y2nRKlInhxQRQ2kgFxGRGqlP5xi+ST9I+k/H\n2Lo3j85trEZHEhEvcs7uYMvGg2zecBD7mSLMZhNxXWLo3qcFDaKDjY4nIgJoIBcRkRrKZDLx7INx\n/HlGGh/9eyezX7wPXx1yKnLbO26zs2ndAbZlHKKk2Il/gA89742l6x+aUz+kjtHxRESuoIFcRERq\nrObRwQzu1ZzUH35j2bosRtzX0uhIImKQEzYHiz7bwp6fjoELgkPr0O2eFtzVtQn+AfqTV0S8kx6d\nRKRCqampOBwO4uPjjY4i4taYgW1Zv/0oKav20Tc+hvBgvQImUpsVnS/huM3OiQI7Bfl2Ttjs5B47\nzbEjpwBoGBNMj76xtItriNmio2bUz0W8mwZyERGp0erV8eXRwe1473+388nyXfx1bBejI4nI71R0\nvpjjNjvH8+0cL7Bz3HaO4/lnOV5wDvuZomu2N5nAGu3PwOGdadoiXCdqE5EaQwO5iIjUeH+8uwkr\nfjzIum1HGdijGR1iI4yOJCLXcb7wwtBdwf9zZx3XbG8yQUhYIA1aRxIWUZewiLqEXrwMC2T7jm00\n0+++iNQwGshFRKTGM5tNPPNAHC++t44Pl+xk1v/pi0WHqooY7nxhcflh5QW2ssvyodtewdBtNhEa\nFkjDmGDCwusSFlm3fPgOCQ3UZ4WLSK2jgVxERGqFVk1CGdCtKSt/zOarjb+R2DvW6Egit4XCc45L\nh5fbLhxifuHrwnPF12xvNpsICQskunFI2cAdXpfQiEDCI+sRHFpHT6aJyG1FA7mIiNQa4wa1ZcOO\nHD5fsZfenRoRGhRgdCQRr+VyuSh2lFLsKMXhKMHhKMVRVFJ2/eJl+fJSih0lly4dpZw6WcgJN0N3\naHggjZqGEn7ZoeVlr3TX0cnWREQu0EAuIhUaMmQImZmZRscQuSnB9fwZO6gtHyzZyfyv9vDC6LuM\njiRy01wuFy4XOJ1OnKUuXC4XTqeL8+dKKcg/e8VQfHFIdlw1RF8cqi/f5prBu7gUXFXPabaUHV4e\n0yyMsIhAwiLqXbisS3CIhm5voX4u4t00kIuISK0ysEczvv0xm9WbD5HQoyltmoYZHUluwuYNB+HC\nQOrCxYV/Zcug7PqF9WWLXRcuy1a6XBe+vnz7i/u5Yh+Xtr+4H9elG+ACXM6yQdjlcuEsLfvaecUy\nZ9n1y9aXb+tyld2+9OJ65zXrnRfWuy77+uLtKrOG3CrV1Ww24etnwc/fh4AAH+oHB5Rd9/MpX+7n\nZ8HXzwc/f8tVX1ewjZ+FOnX9MJt1NnMRkd9DA7mIiNQqFrOJZx7swMtzfuDDJTt554U+WDQ01Bjf\nLPnJ6Ai/m9lsKvtvMWEymS5dN5sxW0yYLWZ8Lqw3X1hvMl/aznRx26uWnT59kgYNrPj5W8oH5ItD\ntZ+fBd8LA/Ol5Ze+tljM+igwEREvpIFcRERqnXbNw7k3Poa1mUdYtSmbgT2aGR1JbtCIsZ0xXXgC\npWx+NHFxjjSZTGCifLC8YvmFy2u2LdtF2boLN7p82bW3u3Sf5cP05YP1ZUO0+cKQe/VgXV0yMzOJ\nj+9YbfsXERHP00AuIiK1UvKQO/nx5/8w/+vd9IyLpn5dP6MjyQ24865GRkcQERHxGJ1tQ0REaqWw\n+gE8ktCaM+eKWbBij9FxRERERK6hgVxEKpSamkp2drbRMUR+lyF/aEHjqHqsSD/ImXMOo+OIiHic\n+rmId6v2Q9bffPNNduzYgclk4m9/+xsdOnQoX7dx40ZmzpyJxWLhnnvu4bnnniMjI4MXXniBli1b\n4nK5aN26NZMmTarumCIiUgv5WMy8+mhXMvfmUq+Or9FxaqSSkhJeeeUVcnJysFgsvPnmm8TExFyx\nzbJly5g/fz4Wi4VRo0YxcuRIAD7++GOWL1+Or68vr7/+Ou3btzfiWxAREfFa1TqQb968mezsbFJS\nUsjKymLixImkpKSUr586dSqffPIJVquVsWPHkpCQAEDXrl2ZNWtWdUYTEZHbROOoIBpHBRkdo8ZK\nTU0lODiYd955hw0bNjB9+nRmzpxZvr6wsJC5c+fyxRdf4OPjw8iRIxkwYAB5eXl88803LF26lL17\n97JmzRoN5CIiIlep1kPW09PT6devHwCxsbGcPn0au90OwOHDhwkJCSEqKgqTyUSfPn348ccfgUuf\nKSoiIiLGuryX9+zZk61bt16xfseOHcTFxVG3bl38/f3p3LkzmZmZrF27lkGDBmEymWjbti3jx483\nIr6IiIhXq9aB3GazERYWVn49NDQUm81W4bqwsDDy8vIAyMrK4rnnnmPMmDFs3LixOiOKiIiIG5f3\n67KP/jJTUlJS4Xoo6+f5+fkcPXqUnJwcnnzySR577DH27t3r8ewiIiLezqMfe+bule+L65o1a8b4\n8eMZNGgQhw8fJikpiVWrVuHjo09oExERqU6LFi1i8eLF5Z/P7XK52Llz5xXbOJ1Ot/twuVyYTCZc\nLhdOp5N58+aRmZnJpEmTWLx4cbVlFxERqYmqdcq1Wq3lr4gD5OXlERkZWb4uPz+/fF1ubi5WqxWr\n1cqgQYMAaNy4MREREeTm5tKokfvPJc3MzKyG76D2UH0qp9pUrGHDhoDqcz2qT+VUm5pn1KhRjBo1\n6oplr776KjabjdatW5e/Mn75k+QV9fO77rqLyMhIWrRoAUB8fDw5OTk3lEE/N+6pPu6pPtdSP78x\nqo97qk/1qdaBvNc2bjUAAAv1SURBVFevXsyZM4eHHnqIXbt2ERUVRWBgIACNGjXCbreTk5OD1Wol\nLS2N6dOns3z5cvLz83n88cfJz8+noKCAqKgot/cTHx9fnd+GiIjIbatXr16sWLGCXr168d1339Gt\nW7cr1nfs2JHXXnuNs2fPYjKZ2LZtGxMnTiQkJISUlBQGDx5MVlYWDRo0uO59qZ+LiMjtxuSq5jOo\nzZgxg4yMDCwWC5MnT2b37t0EBQXRr18/tmzZwjvvvAPAwIEDSU5Oxm63M2HCBM6cOUNJSQnjx4+n\nd+/e1RlRREREKuF0Opk4cSLZ2dn4+/szbdo0oqKi+Oijj+jWrRsdO3bk22+/Zd68eZjNZsaNG8f9\n998PwOzZs9mwYQNQ9kp7x44djfxWREREvE61D+QiIiIiIiIicq1qPcu6iIiIiIiIiFRMA7mIiIiI\niIiIATSQi4iIiIiIiBhAA7mIiIiIiIiIASxvvPHGG0aHuJ79+/czevRoLBYLcXFxALz55pu8//77\nfPHFF7Ru3fqKj0Zbt24dCxYsYN26dTRp0oSQkBCjonvEzdbnq6++YunSpaSmpnLgwIFa/zEzN1sf\nm83GjBkzWLt2LdHR0YSHhxsVvdrdbG3mzJnDsmXL2Lx5MxEREURERBgV3SNutj4A+fn5JCQkkJyc\njMlkMiK2x9xsfbZu3crMmTP55ptviImJwWq1GhW92t1sbbZv387s2bNZvXo10dHRREZGGhW92qmn\nV0793D31c/fU0yunfu6e+rl71d3Tvf4V8sLCQqZMmUKPHj3Kl23evJns7GxSUlKYMmUKU6dOveI2\n69ev59lnnyUxMZFt27Z5OrJHVaU+999/Py+//DJWq5WxY8d6OrJHVaU+ixcvJiYmhoCAgFrdnKpS\nG4CAgABKS0tr/YNvVevz6aefXvM5zbVRVeoTFBTElClTSE5OJiMjw9ORPaYqtQkMDOT111/n0Ucf\nZcuWLZ6O7DHq6ZVTP3dP/dw99fTKqZ+7p37unid6utcP5P7+/sybN++KB4r09HT69esHQGxsLKdP\nn8Zut5evT0hIYPLkyXzwwQf07NnT45k9qSr1ATh48CBhYWEEBgZ6NK+nVaU+OTk5JCQk8PDDD/PZ\nZ595PLOnVKU2Dz/8MC+99BLJycm1ujZQtfosW7aMAQMG4Ofn5/G8nlaV+rRs2ZL09HRmzJhRvl1t\nVJXatGrVCofDwcKFCxk+fLjHM3uKenrl1M/dUz93Tz29curn7qmfu+eJnu5z62PfWmaz+ZpfBpvN\nRvv27cuvh4WFYbPZ+Prrr9m7dy/Hjx9n9uzZFBQUkJKSwvPPP+/p2B5zs/XZt28fEydOJDU1lVGj\nRnk6rsdV5ecnMjISp9NJYGAgRUVFno7sMVX52enXrx9du3YlKCgIh8Ph6cgeVZWfHZfLxaFDh9iz\nZw9fffUVQ4cO9XRsj6nKz09iYiJ9+vQhLi6OOXPm8Nprr3k6tkdUpTZ//vOfefvtt5kwYQL169f3\ndGSPUU+vnPq5e+rn7qmnV0793D31c/c80dO9fiC/EU6nE6C8IX355Ze89dZblJaWMnjwYCOjeYWr\n6wNw5MiRa94rc7u6uj45OTm89957OJ1OnnnmGSOjGe7q2qSlpfHKK6/g6+vL008/bWQ0r1DR7xbA\n0aNHuf/++42I5FWurs/69euZPHkyhYWFJCYmGhnNcFfXZubMmdjtdubOnUuXLl3o37+/kfEMpZ5e\nOfVz99TP3VNPr5z6uXvq5+793p5eIwdyq9WKzWYrv56Xl3fFm+WHDRvGsGHDjIjmFa5XH4Bp06Z5\nOpbXuF59oqOjb9v6XK82ffv2pW/fvgYk8w438rsFZSf6uB1drz69e/emd+/eRkQz3PVq85e//MWI\nWF5BPb1y6ufuqZ+7p55eOfVz99TP3bvVPd3r30NekV69erFy5UoAdu3aRVRUVK1/79TNUH3cU30q\np9q4p/q4p/pUTrWpnGpTOdXGPdXHPdWncqqNe6qPe7e6Pl7/CvmuXbuYNm0aOTk5+Pj4sHLlSubM\nmUO7du3KTz8/efJko2MaRvVxT/WpnGrjnurjnupTOdWmcqpN5VQb91Qf91Sfyqk27qk+7nmiPiaX\ny+W6RXlFRERERERE5AbVyEPWRURERERERGo6DeQiIiIiIiIiBtBALiIiIiIiImIADeQiIiIiIiIi\nBtBALiIiIiIiImIADeQiIiIiIiIiBtBALiIiIiIiImIADeQiXubo0aN06NCBpKQkkpKSGDduHGPG\njGHLli239H7uu+8+Dh8+fMPbL126lC+++KJK97Vs2TIA9u7dy5QpU6q0DxERkZpGPV1ErsfH6AAi\ncq3w8HDmz59ffj0rK4vk5GTWr19/y+7DZDLd1PYPPPBAle4nNzeXlJQUEhMTadOmDZMmTarSfkRE\nRGoi9XQRcUcDuUgNEBsbi8Ph4MSJE3z66ads3bqVoqIi7r77bv76178C8Pe//50dO3ZgtVqJiooi\nLCyMF154gTZt2rB7927MZjNLly4lPT2dt956C5fLBUBhYSEvv/wyp06dwm63k5CQwFNPPUVGRgZz\n584lICCA/v37c+zYMUpKSrjvvvt4++23MZlMlJaWsm3bNr7//nvMZjMvvfQSpaWlnDlzhqSkJIYN\nG8aLL77IL7/8wiuvvMKDD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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcdb7706050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, sharex=True)\n",
    "\n",
    "lasso_results.groupby('alpha')['ic'].mean().plot(logx=True, title='Information Coefficient', ax=axes[0])\n",
    "axes[0].axhline(lasso_results.groupby('alpha')['ic'].mean().median())\n",
    "axes[0].axvline(x=lasso_results.groupby('alpha')['ic'].mean().idxmax(), c='darkgrey', ls='--')\n",
    "axes[0].set_xlabel('Regularization')\n",
    "axes[0].set_ylabel('Information Coefficient')\n",
    "\n",
    "lasso_coeffs.T.plot(legend=False, logx=True, title='Lasso Path', ax=axes[1])\n",
    "axes[1].set_xlabel('Regularization')\n",
    "axes[1].set_ylabel('Coefficients')\n",
    "axes[1].axvline(x=lasso_results.groupby('alpha')['ic'].mean().idxmax(), c='darkgrey', ls='--')\n",
    "fig.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In sum, ridge and lasso will produce similar results. Ridge often computes faster, but lasso also yields continuous features subset selection by gradually reducing coefficients to zero, hence eliminating features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
